in_source_id stringlengths 13 58 | issue stringlengths 3 241k | before_files listlengths 0 3 | after_files listlengths 0 3 | pr_diff stringlengths 109 107M ⌀ |
|---|---|---|---|---|
django-wiki__django-wiki-1299 | Bug: Edit section fails when fenced code block contains comments
### Discussed in https://github.com/django-wiki/django-wiki/discussions/1245
<div type='discussions-op-text'>
<sup>Originally posted by **chrisv2** January 8, 2023</sup>
The following markup will break section editing (due to the `#` in the fenced code block):
````markdown
# Section 1
Section 1 Lorem ipsum dolor sit amet
```python
# hello world
print("hello world")
```
# Section 2
Section 2 Lorem ipsum dolor sit amet
````
# Expected result
When clicking the "edit" link next to "Section 2", the editor should show the following markup:
```markdown
# Section 2
Section 2 Lorem ipsum dolor sit amet
```
# Actual result
The editor shows parts of the fenced code block:
````markdown
# hello world
print("hello world")
```
# Section 2
Section 2 Lorem ipsum dolor sit amet
````
# Notes
Actually it is surprisingly hard to create a proper fix for that, as [it is not possible to get source line numbers from the markdown parser](https://github.com/Python-Markdown/markdown/issues/1235). Currently, the editsection plugin parses the source itself by using regexes, but that doesn't account for fenced code blocks, and it's probably quite difficult to do this with regex alone. IMHO we could do one of the following:
1. loop over markdown source line by line, detecting headers and start/end of fenced code blocks (and possibly other blocks like HTML)
2. create a block level extension and annotate each `h[1-6]` with the source line, then find them in the markup
I'm interested in hacking at this, but I'm not sure which is the best approach. Any thoughts?</div>
| [
{
"content": "from django.urls import re_path as url\nfrom wiki.core.plugins import registry\nfrom wiki.core.plugins.base import BasePlugin\nfrom wiki.plugins.editsection.markdown_extensions import EditSectionExtension\n\nfrom . import settings\nfrom . import views\n\n\nclass EditSectionPlugin(BasePlugin):\n\n ... | [
{
"content": "from django.urls import re_path as url\nfrom wiki.core.plugins import registry\nfrom wiki.core.plugins.base import BasePlugin\nfrom wiki.plugins.editsection.markdown_extensions import EditSectionExtension\n\nfrom . import settings\nfrom . import views\n\n\nclass EditSectionPlugin(BasePlugin):\n\n ... | diff --git a/src/wiki/plugins/editsection/wiki_plugin.py b/src/wiki/plugins/editsection/wiki_plugin.py
index 2987d0c06..506fcf632 100644
--- a/src/wiki/plugins/editsection/wiki_plugin.py
+++ b/src/wiki/plugins/editsection/wiki_plugin.py
@@ -13,7 +13,7 @@ class EditSectionPlugin(BasePlugin):
urlpatterns = {
"article": [
url(
- r"^header/(?P<header>[\w-]+)/$",
+ r"^header/(?P<header>[\S]+)/$",
views.EditSection.as_view(),
name="editsection",
),
diff --git a/tests/plugins/editsection/test_editsection.py b/tests/plugins/editsection/test_editsection.py
index e1c4255e1..be95dd17a 100644
--- a/tests/plugins/editsection/test_editsection.py
+++ b/tests/plugins/editsection/test_editsection.py
@@ -155,6 +155,20 @@ def test_nonunique_headers(self):
expected = "## Date\r\n2023-01-02"
self.assertEqual(actual, expected)
+ def test_underscore_and_dot(self):
+ """test whether we can handle non-slug characters like dots in header IDs"""
+ # Explanation: While autogenerated ids are slugified, Markdown allows to manually
+ # specify the ID using the {#custom_id_value} syntax. As HTML5 only requires ID
+ # values not to contain whitespace, we should be able to handle any valid HTML5 ID, too.
+ source = """# Title 1 {#some_id_with.dot}\n\n"""
+ urlpath = URLPath.create_urlpath(
+ URLPath.root(), "testedit", title="TestEdit", content=source
+ )
+ # rendering causes NoReverseMatch without the fix
+ actual = urlpath.article.render()
+ expected = '<h1 id="some_id_with.dot">Title 1<a class="article-edit-title-link" href="/testedit/_plugin/editsection/header/some_id_with.dot/">[edit]</a></h1>'
+ self.assertEqual(actual, expected)
+
class EditSectionEditBase(RequireRootArticleMixin, FuncBaseMixin):
pass
|
sktime__sktime-5490 | [BUG] Clasp Segmentation sometimes raises a ValueError
**Describe the bug**
<!--
A clear and concise description of what the bug is.
-->
The `predict_scores` method for the `ClaSPSegmentation` algorithm sometimes raises a value error.
**To Reproduce**
<!--
Add a Minimal, Complete, and Verifiable example (for more details, see e.g. https://stackoverflow.com/help/mcve
If the code is too long, feel free to put it in a public gist and link it in the issue: https://gist.github.com
-->
Consider the following `main.py` script.
```python
# main.py
import numpy as np
from sktime.annotation import clasp
# Causes a value error kth(=3) out of bounds (3)
data = np.array([-4.93826793, -5.10968536, -4.94699538, -5.06644812, -5.09389618,
-4.97855996, -5.03805906, -4.94288774, -4.93562747, -5.15704812,
-4.98363671, -4.94730547, -4.95460821, -4.77100651, -4.97104642,
-5.19168259, -4.86592807, -5.03451162, -4.8818181 , -4.96151397,
-5.04680535, -5.10203082, -5.09094031, -5.14550955, -4.88810337,
-4.98522959, -4.89865192, -5.17349344, -4.79173094, -4.98540751,
-4.95218348, -4.92745787, -5.10828999, -5.018588 , -5.23138986,
-5.0829429 , -4.95182016, -5.04750463, -5.04910479, -4.96474658,
-4.87263583, -4.93261922, -4.88780183, -4.99625131, -5.04841316,
-4.8784347 , -4.92589113, -5.23561638, -5.10026717, -4.98502644,
4.95563698, 5.00165565, 5.02851673, 5.09061275, 5.03335048,
5.01256648, 5.21727 , 4.900725 , 4.86488069, 5.03619925,
4.84147019, 4.90967582, 4.94225031, 5.01319431, 4.88011974,
5.03605379, 4.97238513, 4.9344612 , 5.18930892, 4.90348493,
4.83290085, 5.04300367, 4.92500575, 5.06299654, 4.83232756,
4.94786228, 5.03254223, 4.86344949, 4.97827722, 5.03143399,
5.1337682 , 4.88999864, 5.16775253, 5.13377762, 5.09881714,
5.21716965, 4.90141898, 4.94692338, 4.92881945, 4.99632702,
5.14217334, 5.04286422, 5.02892189, 4.8142512 , 4.91298724,
4.97970406, 5.13247509, 4.97386383, 4.94512011, 4.91927367])
model_clasp = clasp.ClaSPSegmentation(n_cps=2)
model_clasp.fit(data)
```
Executing `predict_scores` using this script raises a value error.
```
python3 -i main.py
>>> model_clasp.predict_scores(data)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/alex/documents/sktime/sktime/annotation/base/_base.py", line 150, in predict_scores
return self._predict_scores(X)
File "/home/alex/documents/sktime/sktime/annotation/clasp.py", line 287, in _predict_scores
self.found_cps, self.profiles, self.scores = self._run_clasp(X)
File "/home/alex/documents/sktime/sktime/annotation/clasp.py", line 318, in _run_clasp
self.found_cps, self.profiles, self.scores = _segmentation(
File "/home/alex/documents/sktime/sktime/annotation/clasp.py", line 154, in _segmentation
profile = clasp.transform(X[ranges])
File "/home/alex/documents/sktime/sktime/transformations/base.py", line 583, in transform
Xt = self._transform(X=X_inner, y=y_inner)
File "/home/alex/documents/sktime/sktime/transformations/series/clasp.py", line 106, in _transform
Xt, _ = clasp(
File "/home/alex/documents/sktime/sktime/transformations/series/_clasp_numba.py", line 334, in clasp
knn_mask = _compute_distances_iterative(X, m, k_neighbours).T
File "/home/alex/documents/sktime/sktime/transformations/series/_clasp_numba.py", line 114, in _compute_distances_iterative
idx = np.argpartition(dist, k)
File "/home/alex/documents/sktime/.venv/lib/python3.10/site-packages/numpy/core/fromnumeric.py", line 858, in argpartition
return _wrapfunc(a, 'argpartition', kth, axis=axis, kind=kind, order=order)
File "/home/alex/documents/sktime/.venv/lib/python3.10/site-packages/numpy/core/fromnumeric.py", line 59, in _wrapfunc
return bound(*args, **kwds)
ValueError: kth(=3) out of bounds (3)
```
From debugging, the error seems to come from `_compute_distances_iterative` in `_clasp_number.py` on line 53.
https://github.com/sktime/sktime/blob/f48b1f53f58004bb792abf78ce6818034835d3c2/sktime/transformations/series/_clasp_numba.py#L114
In the example, `k` is `3` and `dist` is `array([inf, inf, inf])`. Since `dist` has length 3, then `k` is out of range. I think this could be solved with some simple bound checking where if `k` is greater than or equal to the length of `dist` then set `k` to the length of `dist` minus 1.
**Expected behavior**
<!--
A clear and concise description of what you expected to happen.
-->
Using `predict_scores` should not raise a value error.
**Additional context**
<!--
Add any other context about the problem here.
-->
This bug happened when I was trying to fit the `ClaSPSegmentation` algorithm to several different data arrays genered using the following code.
```python
n = 50
data = np.concatenate([np.random.normal(-5, 0.1, n), np.random.normal(5, 0.1, n)])
```
**Versions**
<details>
<!--
Please run the following code snippet and paste the output here:
from sktime import show_versions; show_versions()
-->
My versions,
```
>>> from sktime import show_versions; show_versions()
System:
python: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0]
executable: /home/alex/documents/sktime/.venv/bin/python3
machine: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
Python dependencies:
pip: 22.0.2
sktime: 0.24.0
sklearn: 1.3.1
skbase: 0.6.0
numpy: 1.26.1
scipy: 1.11.3
pandas: 2.1.1
matplotlib: 3.8.0
joblib: 1.3.2
numba: 0.58.1
statsmodels: None
pmdarima: None
statsforecast: None
tsfresh: None
tslearn: None
torch: None
tensorflow: None
tensorflow_probability: None
```
<!-- Thanks for contributing! -->
| [
{
"content": "\"\"\"Isolated numba imports for clasp.\"\"\"\n\n\n__author__ = [\"ermshaua\", \"patrickzib\"]\n\nimport numpy as np\nimport pandas as pd\n\nfrom sktime.transformations.panel.matrix_profile import _sliding_dot_products\nfrom sktime.utils.numba.njit import njit\n\n\ndef _sliding_window(X, m):\n ... | [
{
"content": "\"\"\"Isolated numba imports for clasp.\"\"\"\n\n\n__author__ = [\"ermshaua\", \"patrickzib\"]\n\nimport numpy as np\nimport pandas as pd\n\nfrom sktime.transformations.panel.matrix_profile import _sliding_dot_products\nfrom sktime.utils.numba.njit import njit\n\n\ndef _sliding_window(X, m):\n ... | diff --git a/sktime/transformations/series/_clasp_numba.py b/sktime/transformations/series/_clasp_numba.py
index 5ca335ccc99..da2d023b351 100644
--- a/sktime/transformations/series/_clasp_numba.py
+++ b/sktime/transformations/series/_clasp_numba.py
@@ -111,7 +111,8 @@ def _compute_distances_iterative(X, m, k):
)
dist[trivialMatchRange[0] : trivialMatchRange[1]] = np.inf
- idx = np.argpartition(dist, k)
+ _k = min(k, len(dist) - 1)
+ idx = np.argpartition(dist, _k)
knns[order, :] = idx[:k]
dot_prev = dot_rolled
|
ephios-dev__ephios-1244 | API: `/api/users/by_email` returns 404 error for email addresses with dots before the @
**Describe the bug**
A clear and concise description of what the bug is.
**To Reproduce**
Steps to reproduce the behavior:
1. Go to `[ephios-url]/api/users/by_email/vorname.nachname@url.de/`
**Expected behaviour**
Assuming the user exists, the information about the user should be returned.
**Screenshots**
Instead the page 404s.
<img width="1511" alt="Screenshot 2024-03-27 at 18 54 08" src="https://github.com/ephios-dev/ephios/assets/2546622/1383feee-28b0-4825-a31e-c39e2cc3f2ab">
**Environment**
State which device, operating system, browser and browser version you are using.
MacOS 14.2.1 (23C71), Version 17.2.1 (19617.1.17.11.12)
**Additional context**
* The problem does not appear for the test emails `usaaa@localhost/`, `admin@localhost/` or `user@localhost.com`.
| [
{
"content": "from django.db.models import Q\nfrom django.utils import timezone\nfrom django_filters.rest_framework import DjangoFilterBackend\nfrom oauth2_provider.contrib.rest_framework import IsAuthenticatedOrTokenHasScope\nfrom rest_framework import viewsets\nfrom rest_framework.exceptions import Permission... | [
{
"content": "from django.db.models import Q\nfrom django.utils import timezone\nfrom django_filters.rest_framework import DjangoFilterBackend\nfrom oauth2_provider.contrib.rest_framework import IsAuthenticatedOrTokenHasScope\nfrom rest_framework import viewsets\nfrom rest_framework.exceptions import Permission... | diff --git a/ephios/api/views/users.py b/ephios/api/views/users.py
index 110b914a7..9d861b0c5 100644
--- a/ephios/api/views/users.py
+++ b/ephios/api/views/users.py
@@ -96,6 +96,7 @@ class UserByMailView(RetrieveModelMixin, GenericViewSet):
filter_backends = [ObjectPermissionsFilter]
lookup_url_kwarg = "email"
lookup_field = "email"
+ lookup_value_regex = "[^/]+" # customize to allow dots (".") in the lookup value
schema = AutoSchema(operation_id_base="UserProfileByMail")
diff --git a/tests/api/test_user.py b/tests/api/test_user.py
new file mode 100644
index 000000000..8b671092e
--- /dev/null
+++ b/tests/api/test_user.py
@@ -0,0 +1,21 @@
+from django.urls import reverse
+
+
+def test_user_profile_list(django_app, groups, superuser):
+ response = django_app.get(reverse("api:userprofile-list"), user=superuser)
+ assert superuser.email in response
+
+
+def test_api_user_profile_by_email(django_app, superuser):
+ superuser.email = "special-char.123@toplevel.berlin"
+ superuser.save()
+ response = django_app.get(
+ reverse(
+ "api:user-by-email-detail",
+ kwargs={
+ "email": superuser.email,
+ },
+ ),
+ user=superuser,
+ )
+ assert superuser.email in response
|
pex-tool__pex-1194 | Cannot build PEX with relative path for --sources-directory.
Discovered this trying to upgrade Pants to 2.1.26. Looks like:
```
$ mkdir src/
$ echo 'print("Hello World!")' > src/main.py
$ python -m pex -D src -otest.pex -e main
Traceback (most recent call last):
File "/usr/lib/python3.9/runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/jsirois/dev/pantsbuild/jsirois-pex/pex/__main__.py", line 8, in <module>
__name__ == "__main__" and pex.main()
File "/home/jsirois/dev/pantsbuild/jsirois-pex/pex/bin/pex.py", line 1070, in main
pex_builder.freeze(bytecode_compile=options.compile)
File "/home/jsirois/dev/pantsbuild/jsirois-pex/pex/pex_builder.py", line 578, in freeze
self._prepare_code()
File "/home/jsirois/dev/pantsbuild/jsirois-pex/pex/pex_builder.py", line 506, in _prepare_code
self._pex_info.code_hash = CacheHelper.pex_code_hash(self._chroot.path())
File "/home/jsirois/dev/pantsbuild/jsirois-pex/pex/util.py", line 154, in pex_code_hash
return cls._compute_hash(names, stream_factory)
File "/home/jsirois/dev/pantsbuild/jsirois-pex/pex/util.py", line 131, in _compute_hash
with contextlib.closing(stream_factory(name)) as fp:
File "/home/jsirois/dev/pantsbuild/jsirois-pex/pex/util.py", line 152, in stream_factory
return open(os.path.join(d, name), "rb") # noqa: T802
FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmpcb782oi_/main.py'
```
Everything works fine if the sources directory is an absolute path though:
```
$ python -m pex -D $PWD/src -otest.pex -e main
$ ./test.pex
Hello World!
```
| [
{
"content": "# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\nfrom __future__ import absolute_import, print_function\n\nimport atexit\nimport contextlib\nimport errno\nimport fcntl\nimport os\nimport re\nimport shutil\nimport... | [
{
"content": "# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\nfrom __future__ import absolute_import, print_function\n\nimport atexit\nimport contextlib\nimport errno\nimport fcntl\nimport os\nimport re\nimport shutil\nimport... | diff --git a/pex/common.py b/pex/common.py
index e52fd54e3..8181b5323 100644
--- a/pex/common.py
+++ b/pex/common.py
@@ -604,7 +604,7 @@ def symlink(
dst = self._normalize(dst)
self._tag(dst, label)
self._ensure_parent(dst)
- abs_src = src
+ abs_src = os.path.abspath(src)
abs_dst = os.path.join(self.chroot, dst)
os.symlink(abs_src, abs_dst)
diff --git a/tests/test_common.py b/tests/test_common.py
index fa9a02549..ae6fe4b43 100644
--- a/tests/test_common.py
+++ b/tests/test_common.py
@@ -231,6 +231,17 @@ def test_chroot_zip_symlink():
os.path.join(chroot.path(), "directory/subdirectory/file"),
"directory/subdirectory/symlinked",
)
+
+ cwd = os.getcwd()
+ try:
+ os.chdir(os.path.join(chroot.path(), "directory/subdirectory"))
+ chroot.symlink(
+ "file",
+ "directory/subdirectory/rel-symlinked",
+ )
+ finally:
+ os.chdir(cwd)
+
chroot.symlink(os.path.join(chroot.path(), "directory"), "symlinked")
zip_dst = os.path.join(tmp, "chroot.zip")
chroot.zip(zip_dst)
@@ -239,19 +250,23 @@ def test_chroot_zip_symlink():
"directory/",
"directory/subdirectory/",
"directory/subdirectory/file",
+ "directory/subdirectory/rel-symlinked",
"directory/subdirectory/symlinked",
"symlinked/",
"symlinked/subdirectory/",
"symlinked/subdirectory/file",
+ "symlinked/subdirectory/rel-symlinked",
"symlinked/subdirectory/symlinked",
] == sorted(zip.namelist())
assert b"" == zip.read("directory/")
assert b"" == zip.read("directory/subdirectory/")
assert b"data" == zip.read("directory/subdirectory/file")
+ assert b"data" == zip.read("directory/subdirectory/rel-symlinked")
assert b"data" == zip.read("directory/subdirectory/symlinked")
assert b"" == zip.read("symlinked/")
assert b"" == zip.read("symlinked/subdirectory/")
assert b"data" == zip.read("symlinked/subdirectory/file")
+ assert b"data" == zip.read("symlinked/subdirectory/rel-symlinked")
assert b"data" == zip.read("symlinked/subdirectory/symlinked")
diff --git a/tests/test_pex_builder.py b/tests/test_pex_builder.py
index 7d6017495..b25b15eb6 100644
--- a/tests/test_pex_builder.py
+++ b/tests/test_pex_builder.py
@@ -7,8 +7,9 @@
import pytest
-from pex.common import temporary_dir
+from pex.common import safe_open, temporary_dir
from pex.compatibility import WINDOWS, nested
+from pex.executor import Executor
from pex.pex import PEX
from pex.pex_builder import BOOTSTRAP_DIR, CopyMode, PEXBuilder
from pex.testing import make_bdist
@@ -16,7 +17,7 @@
from pex.typing import TYPE_CHECKING
if TYPE_CHECKING:
- from typing import List
+ from typing import Any, Iterator, List
exe_main = """
import sys
@@ -179,7 +180,12 @@ def build_and_check(path, copy_mode):
is_link = (s1[stat.ST_INO], s1[stat.ST_DEV]) == (s2[stat.ST_INO], s2[stat.ST_DEV])
if copy_mode == CopyMode.COPY:
assert not is_link
- elif copy_mode == CopyMode.LINK:
+ else:
+ # Since os.stat follows symlinks; so in CopyMode.SYMLINK, this just proves the
+ # symlink points to the original file. Going further and checking path and
+ # path_clone for the presence of a symlink (an os.islink test) is trickier since
+ # a Linux hardlink of a symlink produces a symlink whereas a maxOS hardlink of a
+ # symlink produces a hardlink.
assert is_link
build_and_check(td2, CopyMode.LINK)
@@ -187,6 +193,38 @@ def build_and_check(path, copy_mode):
build_and_check(td4, CopyMode.SYMLINK)
+@pytest.fixture
+def tmp_chroot(tmpdir):
+ # type: (Any) -> Iterator[str]
+ tmp_chroot = str(tmpdir)
+ cwd = os.getcwd()
+ try:
+ os.chdir(tmp_chroot)
+ yield tmp_chroot
+ finally:
+ os.chdir(cwd)
+
+
+@pytest.mark.parametrize(
+ "copy_mode", [pytest.param(copy_mode, id=copy_mode.value) for copy_mode in CopyMode.values]
+)
+def test_pex_builder_add_source_relpath_issues_1192(
+ tmp_chroot, # type: str
+ copy_mode, # type: CopyMode.Value
+):
+ # type: (...) -> None
+ pb = PEXBuilder(copy_mode=copy_mode)
+ with safe_open("src/main.py", "w") as fp:
+ fp.write("import sys; sys.exit(42)")
+ pb.add_source("src/main.py", "main.py")
+ pb.set_entry_point("main")
+ pb.build("test.pex")
+
+ process = Executor.open_process(cmd=[os.path.abspath("test.pex")])
+ process.wait()
+ assert 42 == process.returncode
+
+
def test_pex_builder_deterministic_timestamp():
# type: () -> None
pb = PEXBuilder()
|
cupy__cupy-2318 | TypeError for OutOfMemoryError
Seen while using chainer while multiprocessing and using the GPU:
```
Traceback (most recent call last):
File "/usr/lib/python3.6/threading.py", line 916, in _bootstrap_inner
self.run()
File "/usr/lib/python3.6/threading.py", line 864, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.6/multiprocessing/pool.py", line 463, in _handle_results
task = get()
File "/usr/lib/python3.6/multiprocessing/connection.py", line 251, in recv
return _ForkingPickler.loads(buf.getbuffer())
File "cupy/cuda/memory.pyx", line 37, in cupy.cuda.memory.OutOfMemoryError.__init__
TypeError: __init__() takes exactly 3 positional arguments (2 given)
```
Seems like it tried to raise an OutOfMemoryError but failed to do so.
```
CuPy Version : 6.1.0
CUDA Root : /usr/local/cuda
CUDA Build Version : 10010
CUDA Driver Version : 10010
CUDA Runtime Version : 10010
cuDNN Build Version : 7500
cuDNN Version : 7500
NCCL Build Version : 2402
NCCL Runtime Version : 2402
```
| [
{
"content": "import hashlib\nimport math\nimport os\nimport re\nimport shutil\nimport sys\nimport tempfile\n\nimport six\n\nfrom cupy.cuda import device\nfrom cupy.cuda import function\nfrom cupy.cuda import nvrtc\n\n_nvrtc_version = None\n_nvrtc_max_compute_capability = None\n\n\ndef _get_nvrtc_version():\n ... | [
{
"content": "import hashlib\nimport math\nimport os\nimport re\nimport shutil\nimport sys\nimport tempfile\n\nimport six\n\nfrom cupy.cuda import device\nfrom cupy.cuda import function\nfrom cupy.cuda import nvrtc\n\n_nvrtc_version = None\n_nvrtc_max_compute_capability = None\n\n\ndef _get_nvrtc_version():\n ... | diff --git a/cupy/cuda/compiler.py b/cupy/cuda/compiler.py
index 9cd1b4ca71a..1c77c13de6f 100644
--- a/cupy/cuda/compiler.py
+++ b/cupy/cuda/compiler.py
@@ -193,6 +193,10 @@ def __init__(self, msg, source, name, options):
self.source = source
self.name = name
self.options = options
+ super(CompileException, self).__init__()
+
+ def __reduce__(self):
+ return (type(self), (self._msg, self.source, self.name, self.options))
def __repr__(self):
return str(self)
diff --git a/cupy/cuda/cublas.pyx b/cupy/cuda/cublas.pyx
index 65fdc55d5a9..67c888d2972 100644
--- a/cupy/cuda/cublas.pyx
+++ b/cupy/cuda/cublas.pyx
@@ -301,6 +301,9 @@ class CUBLASError(RuntimeError):
self.status = status
super(CUBLASError, self).__init__(STATUS[status])
+ def __reduce__(self):
+ return (type(self), (self.status,))
+
@cython.profile(False)
cpdef inline check_status(int status):
diff --git a/cupy/cuda/cudnn.pyx b/cupy/cuda/cudnn.pyx
index 22b2d1d2a94..9dae43a8267 100644
--- a/cupy/cuda/cudnn.pyx
+++ b/cupy/cuda/cudnn.pyx
@@ -760,6 +760,9 @@ class CuDNNError(RuntimeError):
msg = cudnnGetErrorString(<Status>status)
super(CuDNNError, self).__init__(msg.decode())
+ def __reduce__(self):
+ return (type(self), (self.status,))
+
@cython.profile(False)
cpdef inline check_status(int status):
diff --git a/cupy/cuda/cufft.pyx b/cupy/cuda/cufft.pyx
index 593c960427c..23594766999 100644
--- a/cupy/cuda/cufft.pyx
+++ b/cupy/cuda/cufft.pyx
@@ -69,6 +69,9 @@ class CuFFTError(RuntimeError):
self.result = result
super(CuFFTError, self).__init__('%s' % (RESULT[result]))
+ def __reduce__(self):
+ return (type(self), (self.result,))
+
@cython.profile(False)
cpdef inline check_result(int result):
diff --git a/cupy/cuda/curand.pyx b/cupy/cuda/curand.pyx
index 310038c6b5e..1001536df05 100644
--- a/cupy/cuda/curand.pyx
+++ b/cupy/cuda/curand.pyx
@@ -76,6 +76,9 @@ class CURANDError(RuntimeError):
self.status = status
super(CURANDError, self).__init__(STATUS[status])
+ def __reduce__(self):
+ return (type(self), (self.status,))
+
@cython.profile(False)
cpdef inline check_status(int status):
diff --git a/cupy/cuda/cusolver.pyx b/cupy/cuda/cusolver.pyx
index ef24125b885..1d7f9c9c403 100644
--- a/cupy/cuda/cusolver.pyx
+++ b/cupy/cuda/cusolver.pyx
@@ -237,6 +237,9 @@ class CUSOLVERError(RuntimeError):
self.status = status
super(CUSOLVERError, self).__init__(STATUS[status])
+ def __reduce__(self):
+ return (type(self), (self.status,))
+
@cython.profile(False)
cpdef inline check_status(int status):
diff --git a/cupy/cuda/cusparse.pyx b/cupy/cuda/cusparse.pyx
index 772d71d7108..61dd13f190a 100644
--- a/cupy/cuda/cusparse.pyx
+++ b/cupy/cuda/cusparse.pyx
@@ -453,6 +453,9 @@ class CuSparseError(RuntimeError):
self.status = status
super(CuSparseError, self).__init__('%s' % (STATUS[status]))
+ def __reduce__(self):
+ return (type(self), (self.status,))
+
@cython.profile(False)
cpdef inline check_status(int status):
diff --git a/cupy/cuda/cutensor.pyx b/cupy/cuda/cutensor.pyx
index 56245f31feb..b0d18dbff02 100644
--- a/cupy/cuda/cutensor.pyx
+++ b/cupy/cuda/cutensor.pyx
@@ -140,6 +140,9 @@ class CuTensorError(RuntimeError):
msg = cutensorGetErrorString(<Status>status)
super(CuTensorError, self).__init__(msg.decode())
+ def __reduce__(self):
+ return (type(self), (self.status,))
+
@cython.profile(False)
cpdef inline check_status(int status):
diff --git a/cupy/cuda/driver.pyx b/cupy/cuda/driver.pyx
index e8f8cef9557..90f47e79f50 100644
--- a/cupy/cuda/driver.pyx
+++ b/cupy/cuda/driver.pyx
@@ -75,6 +75,9 @@ class CUDADriverError(RuntimeError):
super(CUDADriverError, self).__init__(
'%s: %s' % (s_name.decode(), s_msg.decode()))
+ def __reduce__(self):
+ return (type(self), (self.status,))
+
@cython.profile(False)
cpdef inline check_status(int status):
diff --git a/cupy/cuda/memory.pyx b/cupy/cuda/memory.pyx
index c20fa18d929..49278829680 100644
--- a/cupy/cuda/memory.pyx
+++ b/cupy/cuda/memory.pyx
@@ -43,6 +43,10 @@ class OutOfMemoryError(MemoryError):
"""
def __init__(self, size, total, limit=0):
+ self._size = size
+ self._total = total
+ self._limit = limit
+
if limit == 0:
msg = (
'Out of memory allocating {:,} bytes '
@@ -54,6 +58,9 @@ class OutOfMemoryError(MemoryError):
'limit set to: {:,} bytes).'.format(size, total, limit))
super(OutOfMemoryError, self).__init__(msg)
+ def __reduce__(self):
+ return (type(self), (self._size, self._total, self._limit))
+
@cython.no_gc
cdef class BaseMemory:
diff --git a/cupy/cuda/nccl.pyx b/cupy/cuda/nccl.pyx
index 145e6803b1a..0bae76865ed 100644
--- a/cupy/cuda/nccl.pyx
+++ b/cupy/cuda/nccl.pyx
@@ -101,6 +101,9 @@ class NcclError(RuntimeError):
super(NcclError, self).__init__(
'%s: %s' % (s, msg.decode()))
+ def __reduce__(self):
+ return (type(self), (self.status,))
+
@cython.profile(False)
cpdef inline check_status(ncclResult_t status):
diff --git a/cupy/cuda/nvrtc.pyx b/cupy/cuda/nvrtc.pyx
index 662559956d1..5b4c180585d 100644
--- a/cupy/cuda/nvrtc.pyx
+++ b/cupy/cuda/nvrtc.pyx
@@ -46,6 +46,9 @@ class NVRTCError(RuntimeError):
super(NVRTCError, self).__init__(
'{} ({})'.format(msg.decode(), status))
+ def __reduce__(self):
+ return (type(self), (self.status,))
+
@cython.profile(False)
cpdef inline check_status(int status):
diff --git a/cupy/cuda/runtime.pyx b/cupy/cuda/runtime.pyx
index 3f7e56115c2..6b8365a1b21 100644
--- a/cupy/cuda/runtime.pyx
+++ b/cupy/cuda/runtime.pyx
@@ -138,6 +138,9 @@ class CUDARuntimeError(RuntimeError):
super(CUDARuntimeError, self).__init__(
'%s: %s' % (name.decode(), msg.decode()))
+ def __reduce__(self):
+ return (type(self), (self.status,))
+
@cython.profile(False)
cpdef inline check_status(int status):
diff --git a/tests/cupy_tests/cuda_tests/test_compiler.py b/tests/cupy_tests/cuda_tests/test_compiler.py
index 2e30fec6fbf..82e17e71030 100644
--- a/tests/cupy_tests/cuda_tests/test_compiler.py
+++ b/tests/cupy_tests/cuda_tests/test_compiler.py
@@ -1,3 +1,4 @@
+import pickle
import unittest
import mock
@@ -92,3 +93,12 @@ def test_symbol(self):
def test_space(self):
self.assertFalse(compiler.is_valid_kernel_name('invalid name'))
+
+
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = compiler.CompileException('msg', 'fn.cu', 'fn', ('-ftz=true',))
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_cublas.py b/tests/cupy_tests/cuda_tests/test_cublas.py
new file mode 100644
index 00000000000..dd19eba50f5
--- /dev/null
+++ b/tests/cupy_tests/cuda_tests/test_cublas.py
@@ -0,0 +1,13 @@
+import pickle
+import unittest
+
+from cupy.cuda import cublas
+
+
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = cublas.CUBLASError(1)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_cudnn.py b/tests/cupy_tests/cuda_tests/test_cudnn.py
new file mode 100644
index 00000000000..6458c4fb53a
--- /dev/null
+++ b/tests/cupy_tests/cuda_tests/test_cudnn.py
@@ -0,0 +1,18 @@
+import pickle
+import unittest
+
+try:
+ from cupy.cuda import cudnn
+ cudnn_available = True
+except Exception:
+ cudnn_available = False
+
+
+@unittest.skipUnless(cudnn_available, 'cuDNN is unavailable')
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = cudnn.CuDNNError(1)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_cufft.py b/tests/cupy_tests/cuda_tests/test_cufft.py
new file mode 100644
index 00000000000..7b0a4ccdcc9
--- /dev/null
+++ b/tests/cupy_tests/cuda_tests/test_cufft.py
@@ -0,0 +1,13 @@
+import pickle
+import unittest
+
+from cupy.cuda import cufft
+
+
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = cufft.CuFFTError(1)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_curand.py b/tests/cupy_tests/cuda_tests/test_curand.py
index 42e03764e70..44c36f4f10a 100644
--- a/tests/cupy_tests/cuda_tests/test_curand.py
+++ b/tests/cupy_tests/cuda_tests/test_curand.py
@@ -1,3 +1,4 @@
+import pickle
import unittest
import numpy
@@ -35,3 +36,12 @@ def test_invalid_argument_log_normal_double(self):
with self.assertRaises(ValueError):
curand.generateLogNormalDouble(
self.generator, out.data.ptr, 1, 0.0, 1.0)
+
+
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = curand.CURANDError(100)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_cusolver.py b/tests/cupy_tests/cuda_tests/test_cusolver.py
index 8622ece4c6e..3666a73d82a 100644
--- a/tests/cupy_tests/cuda_tests/test_cusolver.py
+++ b/tests/cupy_tests/cuda_tests/test_cusolver.py
@@ -1,3 +1,4 @@
+import pickle
import unittest
from cupy import cuda
@@ -8,3 +9,13 @@ class TestCusolver(unittest.TestCase):
def test_cusolver_enabled(self):
self.assertEqual(cuda.runtime.runtimeGetVersion() >= 8000,
cuda.cusolver_enabled)
+
+
+@unittest.skipUnless(cuda.cusolver_enabled, 'cuSOLVER is unavailable')
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = cuda.cusolver.CUSOLVERError(1)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_cusparse.py b/tests/cupy_tests/cuda_tests/test_cusparse.py
new file mode 100644
index 00000000000..862edc1ee7a
--- /dev/null
+++ b/tests/cupy_tests/cuda_tests/test_cusparse.py
@@ -0,0 +1,13 @@
+import pickle
+import unittest
+
+from cupy.cuda import cusparse
+
+
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = cusparse.CuSparseError(1)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_cutensor.py b/tests/cupy_tests/cuda_tests/test_cutensor.py
new file mode 100644
index 00000000000..96f4e318f25
--- /dev/null
+++ b/tests/cupy_tests/cuda_tests/test_cutensor.py
@@ -0,0 +1,18 @@
+import pickle
+import unittest
+
+try:
+ from cupy.cuda import cutensor
+ cutensor_available = True
+except Exception:
+ cutensor_available = False
+
+
+@unittest.skipUnless(cutensor_available, 'cuTensor is unavailable')
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = cutensor.CuTensorError(1)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_driver.py b/tests/cupy_tests/cuda_tests/test_driver.py
index fbc1e33d941..a83c65b0508 100644
--- a/tests/cupy_tests/cuda_tests/test_driver.py
+++ b/tests/cupy_tests/cuda_tests/test_driver.py
@@ -1,3 +1,4 @@
+import pickle
import threading
import unittest
@@ -34,3 +35,12 @@ def f(self):
# After the context is created, it should return the valid
# context pointer.
self.assertNotEqual(0, self._result1)
+
+
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = driver.CUDADriverError(1)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_memory.py b/tests/cupy_tests/cuda_tests/test_memory.py
index 7fa4282e96d..7dc3c3eda81 100644
--- a/tests/cupy_tests/cuda_tests/test_memory.py
+++ b/tests/cupy_tests/cuda_tests/test_memory.py
@@ -1,5 +1,6 @@
import ctypes
import gc
+import pickle
import sys
import threading
import unittest
@@ -699,3 +700,12 @@ def test(self):
lock.acquire()
assert gc.isenabled()
self.assertRaises(Exception, lock.release)
+
+
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = memory.OutOfMemoryError(124, 1024, 1024)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_nccl.py b/tests/cupy_tests/cuda_tests/test_nccl.py
index 281e619ed9f..1fac37e83b4 100644
--- a/tests/cupy_tests/cuda_tests/test_nccl.py
+++ b/tests/cupy_tests/cuda_tests/test_nccl.py
@@ -1,3 +1,4 @@
+import pickle
import unittest
from cupy import cuda
@@ -30,3 +31,13 @@ def test_check_async_error(self):
comm = cuda.nccl.NcclCommunicator(1, id, 0)
comm.check_async_error()
comm.destroy()
+
+
+@unittest.skipUnless(cuda.nccl_enabled, 'nccl is not installed')
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = cuda.nccl.NcclError(1)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_nvrtc.py b/tests/cupy_tests/cuda_tests/test_nvrtc.py
new file mode 100644
index 00000000000..ab6a5e9800d
--- /dev/null
+++ b/tests/cupy_tests/cuda_tests/test_nvrtc.py
@@ -0,0 +1,13 @@
+import pickle
+import unittest
+
+from cupy.cuda import nvrtc
+
+
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = nvrtc.NVRTCError(1)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_runtime.pyx b/tests/cupy_tests/cuda_tests/test_runtime.pyx
new file mode 100644
index 00000000000..4d1f0c393ab
--- /dev/null
+++ b/tests/cupy_tests/cuda_tests/test_runtime.pyx
@@ -0,0 +1,13 @@
+import pickle
+import unittest
+
+from cupy.cuda import runtime
+
+
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = runtime.CUDARuntimeError(1)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
|
facebookresearch__hydra-893 | [Bug] Cannot add a value to hydra.job.set_env from the command line.
# 🐛 Bug
## Description
I cannot add a append a config value for the hydra configuration (hydra installed from source)
## To reproduce
**Minimal Code/Config snippet to reproduce**
```
python main.py +hydra.job.env_set.WANDB_NOTES="X"
```
**Stack trace/error message**
```
Error merging override +hydra.job.env_set.WANDB_NOTES="X"
Key 'WANDB_NOTES' is not in struct
full_key: hydra.job.env_set.WANDB_NOTES
reference_type=Dict[str, str]
object_type=dict
```
## Additional context
- I don't have the issue with `+` on non hydra config
- if I add `hydra.job.env_set.WANDB_NOTES=""` in the config then I can correctly override the value (without `+`)
## System information
- **Hydra Version** : 1.0.0rc2
- **Python version** : 3.8.5
- **Operating system** : Ubuntu 18.04.3 LTS
| [
{
"content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nConfiguration loader\n\"\"\"\nimport copy\nimport os\nimport re\nimport warnings\nfrom collections import defaultdict\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, List, Optional, Tuple\n\nfrom omeg... | [
{
"content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nConfiguration loader\n\"\"\"\nimport copy\nimport os\nimport re\nimport warnings\nfrom collections import defaultdict\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, List, Optional, Tuple\n\nfrom omeg... | diff --git a/hydra/_internal/config_loader_impl.py b/hydra/_internal/config_loader_impl.py
index 4a110009b52..b98306a58e5 100644
--- a/hydra/_internal/config_loader_impl.py
+++ b/hydra/_internal/config_loader_impl.py
@@ -265,7 +265,6 @@ def _load_configuration(
)
raise ConfigCompositionException(msg) from e
- OmegaConf.set_struct(cfg.hydra, True)
OmegaConf.set_struct(cfg, strict)
# Apply command line overrides after enabling strict flag
diff --git a/hydra/test_utils/test_utils.py b/hydra/test_utils/test_utils.py
index 5dc62b1ff14..fd1282a0bf9 100644
--- a/hydra/test_utils/test_utils.py
+++ b/hydra/test_utils/test_utils.py
@@ -15,7 +15,7 @@
from subprocess import PIPE, Popen, check_output
from typing import Any, Dict, Iterator, List, Optional, Union
-from omegaconf import DictConfig, OmegaConf
+from omegaconf import Container, DictConfig, OmegaConf
from typing_extensions import Protocol
from hydra._internal.hydra import Hydra
@@ -254,7 +254,7 @@ def _get_statements(indent: str, statements: Union[None, str, List[str]]) -> str
def integration_test(
tmpdir: Path,
- task_config: DictConfig,
+ task_config: Any,
overrides: List[str],
prints: Union[str, List[str]],
expected_outputs: Union[str, List[str]],
@@ -266,7 +266,7 @@ def integration_test(
Path(tmpdir).mkdir(parents=True, exist_ok=True)
if isinstance(expected_outputs, str):
expected_outputs = [expected_outputs]
- if isinstance(task_config, (list, dict)):
+ if not isinstance(task_config, Container):
task_config = OmegaConf.create(task_config)
if isinstance(prints, str):
prints = [prints]
diff --git a/news/854.bugfix b/news/854.bugfix
new file mode 100644
index 00000000000..3b21733f9b2
--- /dev/null
+++ b/news/854.bugfix
@@ -0,0 +1 @@
+Fix overriding of hydra.job.env_set from the command line
diff --git a/tests/test_hydra.py b/tests/test_hydra.py
index 83522ad6f6e..a2ed2555081 100644
--- a/tests/test_hydra.py
+++ b/tests/test_hydra.py
@@ -727,7 +727,7 @@ def test_local_run_workdir(
)
-def test_hydra_env_set(tmpdir: Path) -> None:
+def test_hydra_env_set_with_config(tmpdir: Path) -> None:
cfg = OmegaConf.create({"hydra": {"job": {"env_set": {"foo": "bar"}}}})
integration_test(
tmpdir=tmpdir,
@@ -738,6 +738,16 @@ def test_hydra_env_set(tmpdir: Path) -> None:
)
+def test_hydra_env_set_with_override(tmpdir: Path) -> None:
+ integration_test(
+ tmpdir=tmpdir,
+ task_config={},
+ overrides=["+hydra.job.env_set.foo=bar"],
+ prints="os.environ['foo']",
+ expected_outputs="bar",
+ )
+
+
@pytest.mark.parametrize( # type: ignore
"override", [pytest.param("xyz", id="db=xyz"), pytest.param("", id="db=")]
)
|
open-mmlab__mmdetection3d-600 | votenet pre-trained scannet model doesn't work! KeyError: 'ann_info'
raceback (most recent call last):
File "demo/pcd_demo.py", line 41, in <module>
main()
File "demo/pcd_demo.py", line 28, in main
result, data = inference_detector(model, args.pcd)
File "/home/user/deeplearning/mmdetection3d/mmdet3d/apis/inference.py", line 102, in inference_detector
data = test_pipeline(data)
File "/home/user/deeplearning/mmdetection/mmdet/datasets/pipelines/compose.py", line 40, in __call__
data = t(data)
File "/home/user/deeplearning/mmdetection3d/mmdet3d/datasets/pipelines/transforms_3d.py", line 406, in __call__
assert 'axis_align_matrix' in input_dict['ann_info'].keys(), \
KeyError: 'ann_info'
| [
{
"content": "import mmcv\nimport numpy as np\nimport re\nimport torch\nfrom copy import deepcopy\nfrom mmcv.parallel import collate, scatter\nfrom mmcv.runner import load_checkpoint\nfrom os import path as osp\n\nfrom mmdet3d.core import (Box3DMode, DepthInstance3DBoxes,\n LiDARInstanc... | [
{
"content": "import mmcv\nimport numpy as np\nimport re\nimport torch\nfrom copy import deepcopy\nfrom mmcv.parallel import collate, scatter\nfrom mmcv.runner import load_checkpoint\nfrom os import path as osp\n\nfrom mmdet3d.core import (Box3DMode, DepthInstance3DBoxes,\n LiDARInstanc... | diff --git a/mmdet3d/apis/inference.py b/mmdet3d/apis/inference.py
index 031a242a02..ca0595a65a 100644
--- a/mmdet3d/apis/inference.py
+++ b/mmdet3d/apis/inference.py
@@ -89,6 +89,8 @@ def inference_detector(model, pcd):
pts_filename=pcd,
box_type_3d=box_type_3d,
box_mode_3d=box_mode_3d,
+ # for ScanNet demo we need axis_align_matrix
+ ann_info=dict(axis_align_matrix=np.eye(4)),
sweeps=[],
# set timestamp = 0
timestamp=[0],
|
qutip__qutip-1918 | Bug with Bloch and Ipython.
### Bug Description
`Bloch` raises an error when used in jupyter notebook. This seems to be due to the output of `print_figure` in `_repr_svg_` not being bytecode (maybe it was in the past?) it then defaults to `_repr_png_` and renders correctly the bloch sphere.
### Code to Reproduce the Bug
```shell
import qutip
qutip.Bloch()
```
### Code Output
```shell
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/.virtualenvs/qutip4/lib/python3.10/site-packages/IPython/core/formatters.py in __call__(self, obj)
343 method = get_real_method(obj, self.print_method)
344 if method is not None:
--> 345 return method()
346 return None
347 else:
~/git_repo/qutip/qutip4/qutip/bloch.py in _repr_svg_(self)
293 from IPython.core.pylabtools import print_figure
294 self.render()
--> 295 fig_data = print_figure(self.fig, 'svg').decode('utf-8')
296 plt.close(self.fig)
297 return fig_data
AttributeError: 'str' object has no attribute 'decode'
```
### Expected Behaviour
The Bloch sphere should be plotted correctly without any Error.
### Your Environment
```shell
QuTiP Version: 5.0.0.dev0+ee51e50
Numpy Version: 1.22.3
Scipy Version: 1.8.1
Cython Version: None
Matplotlib Version: 3.5.2
Python Version: 3.10.4
Number of CPUs: 8
BLAS Info: OPENBLAS
OPENMP Installed: False
INTEL MKL Ext: False
Platform Info: Linux (x86_64)
```
### Additional Context
_No response_
| [
{
"content": "__all__ = ['Bloch']\n\nimport os\n\nimport numpy as np\nfrom numpy import (outer, cos, sin, ones)\n\nfrom packaging.version import parse as parse_version\n\nfrom . import Qobj, expect, sigmax, sigmay, sigmaz\n\ntry:\n import matplotlib\n import matplotlib.pyplot as plt\n from mpl_toolkits... | [
{
"content": "__all__ = ['Bloch']\n\nimport os\n\nimport numpy as np\nfrom numpy import (outer, cos, sin, ones)\n\nfrom packaging.version import parse as parse_version\n\nfrom . import Qobj, expect, sigmax, sigmay, sigmaz\n\ntry:\n import matplotlib\n import matplotlib.pyplot as plt\n from mpl_toolkits... | diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 45ea35ea53..8111466dbc 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -100,7 +100,7 @@ jobs:
# rather than in the GitHub Actions file directly, because bash gives us
# a proper programming language to use.
run: |
- QUTIP_TARGET="tests,graphics,semidefinite"
+ QUTIP_TARGET="tests,graphics,semidefinite,ipython"
if [[ -z "${{ matrix.nocython }}" ]]; then
QUTIP_TARGET="$QUTIP_TARGET,runtime_compilation"
fi
diff --git a/qutip/bloch.py b/qutip/bloch.py
index 858b3dd3c4..f525dd0ce0 100644
--- a/qutip/bloch.py
+++ b/qutip/bloch.py
@@ -294,7 +294,7 @@ def _repr_png_(self):
def _repr_svg_(self):
from IPython.core.pylabtools import print_figure
self.render()
- fig_data = print_figure(self.fig, 'svg').decode('utf-8')
+ fig_data = print_figure(self.fig, 'svg')
plt.close(self.fig)
return fig_data
diff --git a/qutip/tests/test_bloch.py b/qutip/tests/test_bloch.py
index 6e8680dd65..f736090c2e 100644
--- a/qutip/tests/test_bloch.py
+++ b/qutip/tests/test_bloch.py
@@ -471,3 +471,10 @@ def test_vector_errors_color_length(self, vectors, colors):
"be equivalent to a 1D array with the same "
"size as the number of vectors. ")
assert str(err.value) == err_msg
+
+
+def test_repr_svg():
+ svg = Bloch()._repr_svg_()
+ assert isinstance(svg, str)
+ assert svg.startswith("<?xml")
+ assert svg.endswith("</svg>\n")
diff --git a/setup.cfg b/setup.cfg
index 5fc2d8202b..c8746a8dc0 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -52,6 +52,8 @@ semidefinite =
tests =
pytest>=5.2
pytest-rerunfailures
+ipython =
+ ipython
; This uses ConfigParser's string interpolation to include all the above
; dependencies into one single target, convenient for testing full builds.
full =
@@ -59,3 +61,4 @@ full =
%(runtime_compilation)s
%(semidefinite)s
%(tests)s
+ %(ipython)s
|
coala__coala-3608 | Remove call_without_output from Shell.py L7
This line was used by the requirement classes, it isnt used anymore as they use sarge, so it should be removed.
difficulty/newcomer
| [
{
"content": "from contextlib import contextmanager\nimport functools\nimport shlex\nfrom subprocess import PIPE, Popen, call, DEVNULL\n\n\ncall_without_output = functools.partial(call, stdout=DEVNULL, stderr=DEVNULL)\n\"\"\"\nUses subprocess.call to execute a command, but suppresses the output and\nthe errors.... | [
{
"content": "from contextlib import contextmanager\nimport shlex\nfrom subprocess import PIPE, Popen\n\n\n@contextmanager\ndef run_interactive_shell_command(command, **kwargs):\n \"\"\"\n Runs a single command in shell and provides stdout, stderr and stdin\n streams.\n\n This function creates a con... | diff --git a/coalib/misc/Shell.py b/coalib/misc/Shell.py
index ec44e45f0b..0bd22886dd 100644
--- a/coalib/misc/Shell.py
+++ b/coalib/misc/Shell.py
@@ -1,14 +1,6 @@
from contextlib import contextmanager
-import functools
import shlex
-from subprocess import PIPE, Popen, call, DEVNULL
-
-
-call_without_output = functools.partial(call, stdout=DEVNULL, stderr=DEVNULL)
-"""
-Uses subprocess.call to execute a command, but suppresses the output and
-the errors.
-"""
+from subprocess import PIPE, Popen
@contextmanager
|
litestar-org__litestar-1641 | Bug: StorageObject doesn't return < 0 when using expiry
### Description
When the stored value is expired, the returned interval is set to 86400 and will therefore not expire.
### URL to code causing the issue
https://github.com/litestar-org/litestar/blob/main/litestar/stores/base.py#L122
### MCVE
```python
from pathlib import Path
from litestar.stores.file import FileStore
store = FileStore(path=Path("test.db"))
async def setstore() -> None:
await store.set("test", "value", expires_in=5)
return None
async def getstore() -> int:
expiry = await store.expires_in("test")
return expiry
```
### Steps to reproduce
_No response_
### Screenshots
```bash
""
```
### Logs
_No response_
### Litestar Version
`litestar==2.0.0a5`
### Platform
- [ ] Linux
- [X] Mac
- [ ] Windows
- [X] Other (Please specify in the description above)
StaticFilesConfig and virtual directories
I'm trying to write a ``FileSystemProtocol`` to load files from the package data using [importlib_resources](https://importlib-resources.readthedocs.io/en/latest/using.html#). But because ``directories`` is defined as ``DirectoryPath``, pydantic checks if the given directories exist in the local filesystem.
This is not generally true, especially in any kind of virtual filesystem (e.g. a zipped package). I think this condition should be relaxed to support virtual filesystems.
https://github.com/starlite-api/starlite/blob/9bb6dcd57c10a591377cf8e3a537e9292566d5b9/starlite/config/static_files.py#L32
| [
{
"content": "from __future__ import annotations\n\nfrom abc import ABC, abstractmethod\nfrom datetime import datetime, timedelta, timezone\nfrom typing import TYPE_CHECKING, Optional\n\nfrom msgspec import Struct\nfrom msgspec.msgpack import decode as msgpack_decode\nfrom msgspec.msgpack import encode as msgpa... | [
{
"content": "from __future__ import annotations\n\nfrom abc import ABC, abstractmethod\nfrom datetime import datetime, timedelta, timezone\nfrom typing import TYPE_CHECKING, Optional\n\nfrom msgspec import Struct\nfrom msgspec.msgpack import decode as msgpack_decode\nfrom msgspec.msgpack import encode as msgpa... | diff --git a/litestar/stores/base.py b/litestar/stores/base.py
index 0cb90b8ccd..b748cf1764 100644
--- a/litestar/stores/base.py
+++ b/litestar/stores/base.py
@@ -119,7 +119,7 @@ def expires_in(self) -> int:
was set, return ``-1``.
"""
if self.expires_at:
- return (self.expires_at - datetime.now(tz=timezone.utc)).seconds
+ return int(self.expires_at.timestamp() - datetime.now(tz=timezone.utc).timestamp())
return -1
def to_bytes(self) -> bytes:
diff --git a/tests/test_stores.py b/tests/test_stores.py
index daefa5a44f..9d630c250b 100644
--- a/tests/test_stores.py
+++ b/tests/test_stores.py
@@ -150,7 +150,11 @@ async def test_delete_all(store: Store) -> None:
assert not any([await store.get(key) for key in keys])
-async def test_expires_in(store: Store) -> None:
+@pytest.mark.usefixtures("patch_storage_obj_frozen_datetime")
+async def test_expires_in(store: Store, frozen_datetime: FrozenDateTimeFactory) -> None:
+ if not isinstance(store, RedisStore):
+ pytest.xfail("bug in FileStore and MemoryStore")
+
assert await store.expires_in("foo") is None
await store.set("foo", "bar")
@@ -159,6 +163,9 @@ async def test_expires_in(store: Store) -> None:
await store.set("foo", "bar", expires_in=10)
assert math.ceil(await store.expires_in("foo") / 10) * 10 == 10 # type: ignore[operator]
+ frozen_datetime.tick(12)
+ assert await store.expires_in("foo") is None
+
@patch("litestar.stores.redis.Redis")
@patch("litestar.stores.redis.ConnectionPool.from_url")
|
gratipay__gratipay.com-3087 | Charges higher than total gifts
For the past three weeks our charges have been about a thousand dollars higher than total gifts.
Charges higher than total gifts
For the past three weeks our charges have been about a thousand dollars higher than total gifts.
| [
{
"content": "\"\"\"This is Gratipay's payday algorithm.\n\nExchanges (moving money between Gratipay and the outside world) and transfers\n(moving money amongst Gratipay users) happen within an isolated event called\npayday. This event has duration (it's not punctiliar).\n\nPayday is designed to be crash-resist... | [
{
"content": "\"\"\"This is Gratipay's payday algorithm.\n\nExchanges (moving money between Gratipay and the outside world) and transfers\n(moving money amongst Gratipay users) happen within an isolated event called\npayday. This event has duration (it's not punctiliar).\n\nPayday is designed to be crash-resist... | diff --git a/branch.sql b/branch.sql
new file mode 100644
index 0000000000..4faca93a2d
--- /dev/null
+++ b/branch.sql
@@ -0,0 +1,34 @@
+BEGIN;
+
+DO $$
+DECLARE
+ payday record;
+ new_ncharges int;
+ new_charge_volume decimal(35,2);
+ new_charge_fees_volume decimal(35,2);
+BEGIN
+ FOR payday IN SELECT * FROM paydays LOOP
+ CREATE TEMP TABLE our_charges AS
+ SELECT *
+ FROM exchanges
+ WHERE "timestamp" >= payday.ts_start
+ AND "timestamp" < payday.ts_end
+ AND amount > 0
+ AND (status IS NULL OR status <> 'failed');
+ new_ncharges := (SELECT count(*) FROM our_charges);
+ new_charge_volume := (SELECT COALESCE(sum(amount + fee), 0) FROM our_charges);
+ new_charge_fees_volume := (SELECT COALESCE(sum(fee), 0) FROM our_charges);
+ UPDATE paydays
+ SET ncharges = new_ncharges
+ , charge_volume = new_charge_volume
+ , charge_fees_volume = new_charge_fees_volume
+ WHERE id = payday.id
+ AND (ncharges <> new_ncharges
+ OR charge_volume <> new_charge_volume
+ OR charge_fees_volume <> new_charge_fees_volume);
+ DROP TABLE our_charges;
+ END LOOP;
+END
+$$;
+
+END;
diff --git a/gratipay/billing/payday.py b/gratipay/billing/payday.py
index 9095491fbe..c774acabb8 100644
--- a/gratipay/billing/payday.py
+++ b/gratipay/billing/payday.py
@@ -656,6 +656,7 @@ def update_stats(self):
SELECT *
FROM our_exchanges
WHERE amount > 0
+ AND status <> 'failed'
)
UPDATE paydays
SET nactive = (
|
ray-project__ray-8782 | [tune] Tune dashboard : use scientific notation
When displaying parameters in the tune dashboard, they are displayer using standard float format
with only 2 significant digits.
For example, if your learning rate is lower than 1e-2, it appears as zero.
Similarly, if your experiments return a metric whose value are in a narrow range, the displayed digits are all equal and you cannot know which one is better (for example, for an accuracy between 0 and 100% treated as a float, values displayed are between 0.00 and 1.00, with a resolution corresponding to 1%, which is really not much).
| [
{
"content": "try:\n import aiohttp.web\nexcept ImportError:\n print(\"The dashboard requires aiohttp to run.\")\n import sys\n sys.exit(1)\n\nimport argparse\nimport copy\nimport datetime\nimport errno\nimport json\nimport logging\nimport os\nimport socket\nimport threading\nimport time\nimport tra... | [
{
"content": "try:\n import aiohttp.web\nexcept ImportError:\n print(\"The dashboard requires aiohttp to run.\")\n import sys\n sys.exit(1)\n\nimport argparse\nimport copy\nimport datetime\nimport errno\nimport json\nimport logging\nimport os\nimport socket\nimport threading\nimport time\nimport tra... | diff --git a/python/ray/dashboard/client/src/common/formatUtils.ts b/python/ray/dashboard/client/src/common/formatUtils.ts
index 582655dce5b90..9518637b5f04c 100644
--- a/python/ray/dashboard/client/src/common/formatUtils.ts
+++ b/python/ray/dashboard/client/src/common/formatUtils.ts
@@ -30,3 +30,18 @@ export const formatDuration = (durationInSeconds: number) => {
`${pad(durationSeconds)}s`,
].join(" ");
};
+
+export const formatValue = (rawFloat: number) => {
+ try {
+ const decimals = rawFloat.toString().split(".")[1].length || 0;
+ if (decimals <= 3) {
+ return rawFloat.toString();
+ } // Few decimals
+ if (Math.abs(rawFloat.valueOf()) >= 1.0) {
+ return rawFloat.toPrecision(5);
+ } // Values >= 1
+ return rawFloat.toExponential(); // Values in (-1; 1)
+ } catch (e) {
+ return rawFloat.toString();
+ }
+};
diff --git a/python/ray/dashboard/client/src/pages/dashboard/tune/TuneTable.tsx b/python/ray/dashboard/client/src/pages/dashboard/tune/TuneTable.tsx
index 7a0479bba3034..ec28743424091 100644
--- a/python/ray/dashboard/client/src/pages/dashboard/tune/TuneTable.tsx
+++ b/python/ray/dashboard/client/src/pages/dashboard/tune/TuneTable.tsx
@@ -21,6 +21,7 @@ import React from "react";
import { connect } from "react-redux";
import { TuneTrial } from "../../../api";
import DialogWithTitle from "../../../common/DialogWithTitle";
+import { formatValue } from "../../../common/formatUtils";
import NumberedLines from "../../../common/NumberedLines";
import { StoreState } from "../../../store";
import { dashboardActions } from "../state";
@@ -387,7 +388,9 @@ class TuneTable extends React.Component<
</TableCell>
{viewableParams.map((value, index) => (
<TableCell className={classes.cell} key={index}>
- {trial["params"][value]}
+ {typeof trial["params"][value] === "number"
+ ? formatValue(Number(trial["params"][value]))
+ : trial["params"][value]}
</TableCell>
))}
<TableCell className={classes.cell}>
@@ -396,7 +399,9 @@ class TuneTable extends React.Component<
{trial["metrics"] &&
viewableMetrics.map((value, index) => (
<TableCell className={classes.cell} key={index}>
- {trial["metrics"][value]}
+ {typeof trial["metrics"][value] === "number"
+ ? formatValue(Number(trial["metrics"][value]))
+ : trial["metrics"][value]}
</TableCell>
))}
<TableCell className={classes.cell}>
diff --git a/python/ray/dashboard/dashboard.py b/python/ray/dashboard/dashboard.py
index 6a3c100052000..d2a84d5b3452f 100644
--- a/python/ray/dashboard/dashboard.py
+++ b/python/ray/dashboard/dashboard.py
@@ -875,7 +875,7 @@ def clean_trials(self, trial_details):
# round all floats
for key in float_keys:
- details[key] = round(details[key], 3)
+ details[key] = round(details[key], 12)
# group together config attributes
for key in config_keys:
|
cobbler__cobbler-2878 | Cannot set property 'file' of image
### Describe the bug
Set image's property 'file' is not working
### Steps to reproduce
1. `touch /tmp/test.iso`
2. Run below Python code on Cobbler installed machine
```
#!/usr/bin/python3
from xmlrpc.client import Server
rpc_server = Server("http://127.0.0.1/cobbler_api")
token = rpc_server.login('cobbler', 'cobbler')
image_handle = rpc_server.new_image(token)
rpc_server.modify_image(image_handle, "name", "testit", token)
rpc_server.modify_image(image_handle, "file", "/tmp/test.iso", token)
rpc_server.save_image(image_handle, token)
img = rpc_server.get_image("testit")
assert(img['file'] == "/tmp/test.iso")
```
### Expected behavior
End without AssertionError
### Cobbler version
<!--- Paste output from `cobbler version` -->
````
Cobbler 3.3.0
source: d8f60bbf, Mon Sep 20 16:55:41 2021 +0200
build time: Thu Dec 23 06:27:43 2021
````
### Operating system
<!--- On which operating system do you use Cobbler? -->
CentOS 8
### Cobbler log
<!--- Paste (partial) output from `/var/log/cobbler/cobbler.log` -->
````paste below
````
### Screenshots
<!--- If applicable, add screenshots to help explain your problem. -->
### Additional information
<!--- Add any other context about the problem here. -->
| [
{
"content": "\"\"\"\nCopyright 2006-2009, Red Hat, Inc and Others\nMichael DeHaan <michael.dehaan AT gmail>\n\nThis program is free software; you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation; either version 2 of the Licens... | [
{
"content": "\"\"\"\nCopyright 2006-2009, Red Hat, Inc and Others\nMichael DeHaan <michael.dehaan AT gmail>\n\nThis program is free software; you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation; either version 2 of the Licens... | diff --git a/cobbler/items/image.py b/cobbler/items/image.py
index 6f5bcea96f..be97b52218 100644
--- a/cobbler/items/image.py
+++ b/cobbler/items/image.py
@@ -206,6 +206,8 @@ def file(self, filename: str):
if len(auth) > 0 and len(hostname) == 0:
raise SyntaxError("a hostname must be specified with authentication details")
+ self._file = uri
+
@property
def os_version(self) -> str:
r"""
diff --git a/tests/items/image_test.py b/tests/items/image_test.py
index f8bc0eaf19..4a8f833965 100644
--- a/tests/items/image_test.py
+++ b/tests/items/image_test.py
@@ -56,10 +56,10 @@ def test_file():
image = Image(test_api)
# Act
- image.file = ""
+ image.file = "/tmp/test"
# Assert
- assert image.file == ""
+ assert image.file == "/tmp/test"
def test_os_version():
|
pypa__setuptools-4127 | [BUG] Setuptools 69.0.0 breaks Astropy's setup
### setuptools version
setuptools==69.0.0
### Python version
3.12
### OS
Ubuntu
### Additional environment information
_No response_
### Description
About 15h ago, Astropy's CI started failing to build with
```
ImportError: cannot import name 'newer_group' from 'setuptools.dep_util'
```
This seems to correspond to an [intentional change in setuptools 69](https://setuptools.pypa.io/en/latest/history.html#features).
Nonetheless, from reading the PR that introduced the change (https://github.com/pypa/setuptools/pull/4069), I'm not sure that this was supposed to break immediately. Was this intended ?
### Expected behavior
a deprecation warning instead of a hard error ?
### How to Reproduce
```shell
$ python -c "from setuptools.dep_util import newer_group"
```
### Output
```console
Traceback (most recent call last):
File "<string>", line 1, in <module>
ImportError: cannot import name 'newer_group' from 'setuptools.dep_util' (/private/tmp/venv/lib/python3.12/site-packages/setuptools/dep_util.py)
```
| [
{
"content": "import warnings\n\nfrom ._distutils import _modified\n\n\ndef __getattr__(name):\n if name not in ['newer_pairwise_group']:\n raise AttributeError(name)\n warnings.warn(\n \"dep_util is Deprecated. Use functions from setuptools.modified instead.\",\n DeprecationWarning,\... | [
{
"content": "import warnings\n\nfrom ._distutils import _modified\n\n\ndef __getattr__(name):\n if name not in ['newer_group', 'newer_pairwise_group']:\n raise AttributeError(name)\n warnings.warn(\n \"dep_util is Deprecated. Use functions from setuptools.modified instead.\",\n Depre... | diff --git a/newsfragments/4126.bugfix.rst b/newsfragments/4126.bugfix.rst
new file mode 100644
index 0000000000..467a94887a
--- /dev/null
+++ b/newsfragments/4126.bugfix.rst
@@ -0,0 +1,2 @@
+Fixed imports of ``setuptools.dep_util.newer_group``.
+A deprecation warning is issued instead of a hard failure.
diff --git a/setuptools/dep_util.py b/setuptools/dep_util.py
index e30cd41b49..c8ab14c8f2 100644
--- a/setuptools/dep_util.py
+++ b/setuptools/dep_util.py
@@ -4,7 +4,7 @@
def __getattr__(name):
- if name not in ['newer_pairwise_group']:
+ if name not in ['newer_group', 'newer_pairwise_group']:
raise AttributeError(name)
warnings.warn(
"dep_util is Deprecated. Use functions from setuptools.modified instead.",
|
WordPress__openverse-api-788 | Update the auth token expiry to be 12 or 24 hours
## Problem
Currently the token returned from the auth_tokens/token route returns a token that expires in 10 hours. To make this a little more consistent with typical CRON scheduling it would be more ideal for this expiry to be 12 or 24 hours.
## Description
Updating the auth token expires_in time would allow for more consistent CRON scheduling and most users would more typically expect it to expire every 12/24 hours.
## Alternatives
No suggestions aside from even longer expiry times (maybe once a year or once a month, etc).
## Additional context
Issue/suggestion originally came up here: [https://github.com/WordPress/openverse-api/issues/770](https://github.com/WordPress/openverse-api/issues/770)
## Implementation
- [ ] 🙋 I would be interested in implementing this feature.
| [
{
"content": "\"\"\"\nDjango settings for catalog project.\n\nGenerated by 'django-admin startproject' using Django 2.0.5.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/2.0/topics/settings/\n\nFor the full list of settings and their values, see\nhttps://docs.djangoproject.com/en/2... | [
{
"content": "\"\"\"\nDjango settings for catalog project.\n\nGenerated by 'django-admin startproject' using Django 2.0.5.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/2.0/topics/settings/\n\nFor the full list of settings and their values, see\nhttps://docs.djangoproject.com/en/2... | diff --git a/api/catalog/settings.py b/api/catalog/settings.py
index 4c915d5fa..fc293d5d0 100644
--- a/api/catalog/settings.py
+++ b/api/catalog/settings.py
@@ -113,7 +113,10 @@
"SCOPES": {
"read": "Read scope",
"write": "Write scope",
- }
+ },
+ "ACCESS_TOKEN_EXPIRE_SECONDS": config(
+ "ACCESS_TOKEN_EXPIRE_SECONDS", default=3600 * 12, cast=int
+ ),
}
OAUTH2_PROVIDER_APPLICATION_MODEL = "api.ThrottledApplication"
|
azavea__raster-vision-1586 | Same explanation for SlidingWindowGeoDataset and RandomWindowGeoDataset
## 📚 Documentation
<!-- A clear and concise description of what content in https://docs.rastervision.io/ is an issue.-->
> The SlidingWindowGeoDataset allows reading the scene by sampling random window sizes and locations.
This description is same to explained both SlidingWindowGeoDataset and RandomWindowGeoDataset. This can be found here: https://docs.rastervision.io/en/latest/tutorials/sampling_training_data.html
| [
{
"content": "from typing import List, Optional, Tuple, Union\n\nfrom rastervision.pipeline.config import (Config, register_config, ConfigError,\n Field, validator)\nfrom rastervision.core.data.utils import color_to_triple, normalize_color\n\nDEFAULT_NULL_CLASS_NAME = 'n... | [
{
"content": "from typing import List, Optional, Tuple, Union\n\nfrom rastervision.pipeline.config import (Config, register_config, ConfigError,\n Field, validator)\nfrom rastervision.core.data.utils import color_to_triple, normalize_color\n\nDEFAULT_NULL_CLASS_NAME = 'n... | diff --git a/docs/README.md b/docs/README.md
index 1045bcba4..2093525f9 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -46,6 +46,7 @@ To run a live local server that updates with changes, run:
- You can specify a thumbnail for a notebook (which is shown in the gallery) in the following ways:
- To use the output of a cell in that notebook, add a `nbsphinx-thumbnail` tag to that cell's metadata. If the cell has multiple image outputs, [see instructions here](https://nbsphinx.readthedocs.io/en/latest/gallery/multiple-outputs.html). **Note:** this ONLY works with outputs of *code* cells.
- To use an arbitrary image, add it to the [`img/`](./img/) directory and then add its path to the `nbsphinx_thumbnails` `dict` in [`conf.py`](./conf.py).
+- *Do* use cross-references and other Sphinx-styling in notebooks. You can do so by [using raw `reST` cells](https://nbsphinx.readthedocs.io/en/0.8.10/raw-cells.html#reST). See existing notebooks for examples.
- The furo theme [allows specifying different versions of the same image for light and dark modes](https://pradyunsg.me/furo/reference/images/#different-images-for-dark-light-mode) by using the `:class: only-light` and `:class: only-dark` options with the `.. image::` directive. Instead of specifying different images, we specify the same image for both and use CSS-based color-inversion for dark mode. This saves us the trouble of creating dark versions for each image.
- This should only be applied to images where it makes sense to invert in dark mode.
- Where it makes sense to invert in dark mode but inversion does not produce a good result, it is recommended to manually create a better looking dark-mode version of the image.
diff --git a/docs/usage/tutorials/lightning_workflow.ipynb b/docs/usage/tutorials/lightning_workflow.ipynb
index f98a71ea4..29c4f5158 100644
--- a/docs/usage/tutorials/lightning_workflow.ipynb
+++ b/docs/usage/tutorials/lightning_workflow.ipynb
@@ -11,7 +11,7 @@
"\n",
"[Lightning](https://www.pytorchlightning.ai/) (formerly known as PyTorch Lightning) is a high-level library for training PyTorch models. In this tutorial, we demonstrate a complete workflow for doing semantic segmentation on SpaceNet Vegas using a combination of Raster Vision and Lightning. We use Raster Vision for reading data, Lightning for training a model, and then Raster Vision again for making predictions and evaluations on whole scenes. \n",
"\n",
- "Raster Vision has easy-to-use, built-in model training functionality implemented by the `Learner` class which is shown in the [`train.ipynb`](./train.ipynb) notebook. However, some users may prefer to use Lightning for training models, either because they already know how to use it, and like it, or because they desire more flexibility than the `Learner` class offers. This notebook shows how these libraries can be used together, but does not attempt to use either library in a particularly sophisticated manner."
+ "Raster Vision has easy-to-use, built-in model training functionality implemented by the `Learner` class which is shown in the [\"Training a model\" tutorial](./train.ipynb). However, some users may prefer to use Lightning for training models, either because they already know how to use it, and like it, or because they desire more flexibility than the `Learner` class offers. This notebook shows how these libraries can be used together, but does not attempt to use either library in a particularly sophisticated manner."
]
},
{
@@ -309,18 +309,21 @@
"source": [
"## Monitor training using Tensorboard\n",
"\n",
- "This runs an instance of Tensorboard inside this notebook.\n",
- "\n",
- "<div class=\"alert alert-info\">\n",
- "\n",
- "Note\n",
- "\n",
- "<ul>\n",
- " <li>If running inside the Raster Vision docker image, you will need to pass `--tensorboard` to `docker/run` for this to work.</li>\n",
- " <li>If the dashboard doen't auto-reload, you can click the reload button onthe top-right.</li>\n",
- "</ul>\n",
+ "This runs an instance of Tensorboard inside this notebook."
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "5934b7b3-f25e-4278-a371-2b5a2f93a0d0",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. note::\n",
"\n",
- "</div>"
+ " - If running inside the Raster Vision docker image, you will need to pass `--tensorboard` to `docker/run` for this to work.\n",
+ " - If the dashboard doen't auto-reload, you can click the reload button on the top-right.\n"
]
},
{
@@ -495,11 +498,33 @@
{
"cell_type": "markdown",
"id": "334768ff",
- "metadata": {},
+ "metadata": {
+ "tags": []
+ },
"source": [
- "## Make predictions for scene\n",
- "\n",
- "We can now use Raster Vision's `SemanticSegmentationLabels` class to make predictions over a whole scene. The `SemanticSegmentationLabels.from_predictions` method takes an iterator over predictions. We create this using a `get_predictions` helper function below."
+ "## Make predictions for scene"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "ab6da8e6-5bbd-4687-b672-cf1070d9d51a",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core.data.label.semantic_segmentation_labels"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "3d4fe6c2-1683-47a9-97d1-119d94980005",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "We can now use Raster Vision's :class:`SemanticSegmentationLabels` class to make predictions over a whole scene. The :meth:`SemanticSegmentationLabels.from_predictions` method takes an iterator over predictions. We create this using a ``get_predictions()`` helper function defined below."
]
},
{
@@ -644,9 +669,29 @@
"id": "bfb00ec1",
"metadata": {},
"source": [
- "## Evaluate predictions for a scene\n",
- "\n",
- "Now that we have predictions for the validation scene, we can evaluate them by comparing to ground truth using `SemanticSegmentationEvaluator`."
+ "## Evaluate predictions for a scene"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "a16c1d55-4ebb-4e06-aede-bcb8806ca6b4",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core.evaluation.semantic_segmentation_evaluator"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "89bcd5d6-c352-45eb-ac80-4ded6c4136e5",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "Now that we have predictions for the validation scene, we can evaluate them by comparing to ground truth using :class:`SemanticSegmentationEvaluator`."
]
},
{
@@ -665,6 +710,28 @@
" predictions=pred_labels)"
]
},
+ {
+ "cell_type": "raw",
+ "id": "f412a014-505c-4b85-8d33-759b6ec67000",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core.evaluation"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "82af492a-c739-432f-bb9a-e333fb63c9de",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ":meth:`SemanticSegmentationEvaluator.evaluate_predictions() <semantic_segmentation_evaluator.SemanticSegmentationEvaluator.evaluate_predictions>` returns a :class:`~semantic_segmentation_evaluation.SemanticSegmentationEvaluation` object which contains evaluations for each class as :class:`~class_evaluation_item.ClassEvaluationItem` objects."
+ ]
+ },
{
"cell_type": "markdown",
"id": "31d7fcd4",
@@ -764,7 +831,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3.9.10 ('base')",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
diff --git a/docs/usage/tutorials/pred_and_eval_ss.ipynb b/docs/usage/tutorials/pred_and_eval_ss.ipynb
index 5ea62f612..2ad5c11d2 100644
--- a/docs/usage/tutorials/pred_and_eval_ss.ipynb
+++ b/docs/usage/tutorials/pred_and_eval_ss.ipynb
@@ -11,11 +11,26 @@
]
},
{
- "cell_type": "markdown",
- "id": "cbbac9ba-59f2-40d2-9a17-7fd987157c10",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "c6aad8b5-a4ab-401a-a1a4-c7ffcb670fa4",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.pytorch_learner.learner"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "a101fa91-1d00-46fc-841d-c1996db796ee",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "## Load `Learner` with trained model from bundle"
+ "Load a :class:`Learner` with a trained model from bundle -- :meth:`Learner.from_model_bundle`\n",
+ "---------------------------------------------------------------------------------------------"
]
},
{
@@ -117,11 +132,26 @@
]
},
{
- "cell_type": "markdown",
- "id": "8a3c055f-917f-4c89-a95a-2139d8982f3a",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "09323795-e507-496d-ae6f-0bea8e98625a",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "## Predict"
+ "Predict -- :meth:`Learner.predict_dataset`\n",
+ "------------------------------------------"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "9f65bed3-b69c-4198-ba11-d3ecd67ccd7c",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "Make predictions via :meth:`Learner.predict_dataset` and then turn them into `Labels <reading_labels.ipynb#Labels>`_ via :meth:`Labels.from_predictions <rastervision.core.data.label.labels.Labels.from_predictions>` (specifically, :meth:`SemanticSegmentationLabels.from_predictions <rastervision.core.data.label.semantic_segmentation_labels.SemanticSegmentationLabels.from_predictions>`)."
]
},
{
@@ -171,6 +201,32 @@
"## Visualize predictions"
]
},
+ {
+ "cell_type": "raw",
+ "id": "724ebc4e-f2da-47dc-8e44-d20c8a2cccf1",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core.data.label.semantic_segmentation_labels"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "bc6c1ced-db14-42be-8212-319ee5426491",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "``pred_labels`` is an instance of :class:`~SemanticSegmentationSmoothLabels` which is a raster of probability distributions for each pixel for the entire scene. We can get these probabilities via :meth:`~SemanticSegmentationSmoothLabels.get_score_arr`.\n",
+ "\n",
+ ".. note::\n",
+ "\n",
+ " There is also a :meth:`~SemanticSegmentationSmoothLabels.get_label_arr` method that will return a 2D raster of class IDs representing the most probable class for each pixel."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 7,
@@ -220,11 +276,26 @@
]
},
{
- "cell_type": "markdown",
- "id": "bfdcab71-e786-484b-8667-bde704870be2",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "1e2018a7-06f1-4bb6-a892-17a7f848d192",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "## Save predictions to file"
+ ".. currentmodule:: rastervision.core.data"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "aa11cb15-4fcc-428e-9c54-fb5c38af8e88",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "Save predictions to file -- :meth:`SemanticSegmentationSmoothLabels.save() <label.semantic_segmentation_labels.SemanticSegmentationSmoothLabels.save>`\n",
+ "------------------------------------------------------------------------------------------------------------------------------------------------------"
]
},
{
@@ -272,16 +343,46 @@
{
"cell_type": "markdown",
"id": "6ea5b49f-3438-41f9-9438-a5b1b3594493",
- "metadata": {},
+ "metadata": {
+ "tags": []
+ },
"source": [
"## Evaluate predictions"
]
},
+ {
+ "cell_type": "raw",
+ "id": "8f10aac0-c0b2-45d2-9b20-069d25756ba6",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "7cedf0c4-0900-47d6-855a-78658b7896b4",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "We now want to evaluate the predictions against the ground truth labels.\n",
+ "\n",
+ "Raster Vision allows us to do this via an :class:`~evaluation.evaluator.Evaluator`. In our case, this would be the :class:`~evaluation.semantic_segmentation_evaluator.SemanticSegmentationEvaluator`. We are going to use its :meth:`~evaluation.semantic_segmentation_evaluator.SemanticSegmentationEvaluator.evaluate_predictions` method, which takes both ground truth labels and predictions as :class:`~data.label.labels.Labels` objects.\n",
+ "\n",
+ "We already have the predictions as a :class:`~data.label.semantic_segmentation_labels.SemanticSegmentationLabels` object, so we just need to load the ground truth labels as :class:`~data.label.semantic_segmentation_labels.SemanticSegmentationLabels` too. We do that by using :func:`~data.utils.factory.make_ss_scene` factory function to create a scene and then accessing ``scene.label_source.get_labels()``. Alternatively, we could have directly created a :class:`~data.label_source.semantic_segmentation_label_source.SemanticSegmentationLabelSource`."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 10,
"id": "ff500453-d315-45de-b538-113bd43328b6",
- "metadata": {},
+ "metadata": {
+ "tags": []
+ },
"outputs": [
{
"name": "stderr",
@@ -301,7 +402,22 @@
" label_vector_default_class_id=class_config.get_class_id('building'),\n",
" label_raster_source_kw=dict(\n",
" background_class_id=class_config.get_class_id('background')),\n",
- " image_raster_source_kw=dict(allow_streaming=True))"
+ " image_raster_source_kw=dict(allow_streaming=True))\n",
+ "\n",
+ "gt_labels = scene.label_source.get_labels()"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "b82f298e-e194-48e2-a9bf-a6322003730d",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. note::\n",
+ "\n",
+ " ``gt_labels`` is an instance of :class:`~data.label.semantic_segmentation_labels.SemanticSegmentationDiscreteLabels`. You can convert it to a label raster (for visualization or some other analysis) via :meth:`~data.label.semantic_segmentation_labels.SemanticSegmentationDiscreteLabels.get_label_arr`."
]
},
{
@@ -316,8 +432,31 @@
"evaluator = SemanticSegmentationEvaluator(class_config)\n",
"\n",
"evaluation = evaluator.evaluate_predictions(\n",
- " ground_truth=scene.label_source.get_labels(),\n",
- " predictions=pred_labels)"
+ " ground_truth=gt_labels, predictions=pred_labels)"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "822bc6fe-0de2-4aa5-99b3-aeda4dd2221e",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core.evaluation"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "f33e9604-3333-4dd1-9f79-08174e3e73fc",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ":meth:`SemanticSegmentationEvaluator.evaluate_predictions() <semantic_segmentation_evaluator.SemanticSegmentationEvaluator.evaluate_predictions>` returns a :class:`~semantic_segmentation_evaluation.SemanticSegmentationEvaluation` object which contains evaluations for each class as :class:`~class_evaluation_item.ClassEvaluationItem` objects.\n",
+ "\n",
+ "We can examine these evaluations as shown below."
]
},
{
@@ -422,6 +561,17 @@
"### Save evaluation"
]
},
+ {
+ "cell_type": "raw",
+ "id": "0f9cb43e-24f1-45bb-abe1-6ac2e23c70f6",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "We can also save the evaluations as a JSON via :meth:`SemanticSegmentationEvaluation.save() <semantic_segmentation_evaluation.SemanticSegmentationEvaluation.save>`"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 14,
diff --git a/docs/usage/tutorials/reading_labels.ipynb b/docs/usage/tutorials/reading_labels.ipynb
index 50062d0a3..6fc7dd803 100644
--- a/docs/usage/tutorials/reading_labels.ipynb
+++ b/docs/usage/tutorials/reading_labels.ipynb
@@ -12,7 +12,6 @@
"cell_type": "markdown",
"id": "4431879d-e81d-4994-b55a-e35392d3387b",
"metadata": {
- "jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
@@ -24,9 +23,20 @@
"id": "c9bb2d1e-bda0-4f3d-b7a6-e9362ba43878",
"metadata": {},
"source": [
- "Before we can work with labels, we first want to define what our target classes are -- their names, their IDs, and possibly, their colors (to use for visualization).\n",
+ "Before we can work with labels, we first want to define what our target classes are -- their names, their IDs, and possibly, their colors (to use for visualization)."
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "a68160d7-e2ba-4da2-8925-8a0b565865fc",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core.data\n",
"\n",
- "Raster Vision makes all this simple to do using the handy `ClassConfig` class."
+ "Raster Vision makes all this simple to do using the handy :class:`~class_config.ClassConfig` class."
]
},
{
@@ -55,11 +65,28 @@
},
{
"cell_type": "markdown",
- "id": "9dd006f6-9f15-4825-be2c-2bdff84c3109",
+ "id": "a06bb9e2-c44c-4e7b-a499-898e305e349d",
+ "metadata": {},
+ "source": [
+ "(Note how a unique color was generated for each class.)"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "aaf7c95f-ccc7-4b84-9a7c-d96ae25142c1",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "The numeric ID of each class is its index in the :attr:`~class_config.ClassConfig.names` list."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c1d973a4-1da7-4a06-ba64-0bf4f670c17e",
"metadata": {},
"source": [
- "The numeric ID of each class is its index in the `ClassConfig.names` list.\n",
- "\n",
"We can query the ID of a class like so:"
]
},
@@ -250,13 +277,22 @@
"### Normalized colors"
]
},
+ {
+ "cell_type": "raw",
+ "id": "be575610-a6ad-416d-8427-2b9ab0f38292",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "Another nifty functionality is the :attr:`~class_config.ClassConfig.color_triples` property that returns the colors in a normalized form that can be directly used with ``matplotlib``."
+ ]
+ },
{
"cell_type": "markdown",
"id": "6982066d-15bb-4cdd-aa4a-a321fec57df4",
"metadata": {},
"source": [
- "Another nifty functionality is the `ClassConfig.color_triples` property that returns the colors in a normalized form that can be directly used with `matplotlib`.\n",
- "\n",
"The example below shows how we can easily create a color-map from our class colors."
]
},
@@ -308,13 +344,18 @@
]
},
{
- "cell_type": "markdown",
- "id": "a302dda8-e545-4142-a332-3a47f2abf89a",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "532dc6a7-5a16-4632-9cc6-46404c822761",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "While `RasterSource`s and `VectorSource`s allow us to read raw data, the `LabelSource`s take this data and convert them into a form suitable for machine learning.\n",
+ ".. currentmodule:: rastervision.core.data.label_source\n",
+ "\n",
+ "While `RasterSources <reading_raster_data.ipynb>`_ and `VectorSources <reading_vector_data.ipynb>`_ allow us to read raw data, the :class:`LabelSources <label_source.LabelSource>` take this data and convert them into a form suitable for machine learning.\n",
"\n",
- "We have 3 kinds of pre-defined `LabelSource`s -- one for each of the 3 main computer vision tasks: semantic segmentation, object detection, and chip classification."
+ "We have 3 kinds of pre-defined :class:`LabelSources <label_source.LabelSource>` -- one for each of the 3 main computer vision tasks: semantic segmentation, object detection, and chip classification."
]
},
{
@@ -333,13 +374,22 @@
"### Semantic Segmentation - `SemanticSegmentationLabelSource`"
]
},
+ {
+ "cell_type": "raw",
+ "id": "42385eeb-1ac9-497b-bd31-a514a7bae055",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ":class:`~semantic_segmentation_label_source.SemanticSegmentationLabelSource` is perhaps the simplest of `LabelSources`. Since semantic segmentation labels are rasters themselves, :class:`~semantic_segmentation_label_source.SemanticSegmentationLabelSource` takes in a `RasterSource` and allows querying chips from it using array-indexing or :class:`Box's <rastervision.core.box.Box>`."
+ ]
+ },
{
"cell_type": "markdown",
- "id": "0bd3566f-ece9-400e-9dfe-0b81b03bb112",
+ "id": "d9e5cab5-2d59-4903-99e0-faebc86d82da",
"metadata": {},
"source": [
- "`SemanticSegmentationLabelSource` is perhaps the simplest of `LabelSource`. Since semantic segmentation labels are rasters themselves, `SemanticSegmentationLabelSource` takes in a `RasterSource` and allows querying chips from it using array-indexing or `Box`'s. \n",
- "\n",
"The main added service it provides is ensuring that if a chip overflows the extent, the overflowing pixels are assigned the ID of the \"null class\"."
]
},
@@ -394,14 +444,20 @@
]
},
{
- "cell_type": "markdown",
- "id": "23e4671c-b035-4839-9b78-59c007cddb48",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "a7d76d20-12b5-46d8-82cb-58afed008eab",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
+ ".. currentmodule:: rastervision.core.data\n",
+ "\n",
"Create\n",
- "- a `RasterSource` to get the image extent and a `CRSTRansformer`\n",
- "- a `VectorSource` to read the vector labels\n",
- "- a `RasterizedSource` to rasterize the `VectorSource`"
+ "\n",
+ "- a :class:`~raster_source.raster_source.RasterSource` to get the image extent and a :class:`~crs_transformer.crs_transformer.CRSTransformer`\n",
+ "- a :class:`~vector_source.vector_source.VectorSource` to read the vector labels\n",
+ "- a :class:`~raster_source.rasterized_source.RasterizedSource` to rasterize the :class:`~vector_source.vector_source.VectorSource`"
]
},
{
@@ -539,13 +595,24 @@
"### Object Detection - `ObjectDetectionLabelSource`"
]
},
+ {
+ "cell_type": "raw",
+ "id": "a8d6e198-2b77-414d-b4d4-5f7060481968",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core.data.label_source\n",
+ "\n",
+ "The :class:`~object_detection_label_source.ObjectDetectionLabelSource` allows querying all the label bounding boxes and their corresponding class IDs that fall inside a window. It also transforms the coordinates of the returned bounding boxes so that they represent points inside the window rather than the global extent."
+ ]
+ },
{
"cell_type": "markdown",
- "id": "4f325b0c-5b1b-4d45-ba3b-d41973257a49",
+ "id": "d26cb537-efdb-4004-b57c-363dcf8aae80",
"metadata": {},
"source": [
- "The `ObjectDetectionLabelSource` allows querying all the label bounding boxes and their corresponding class IDs that fall inside a window. It also transforms the coordinates of the returned bounding boxes so that they represent points inside the window rather than the global extent.\n",
- "\n",
"The bounding-box-to-window matching behavior can be further controlled by specifying an `ioa_thresh` (intersection-over-area threshold for considering a bounding box a part of a window) and a `clip` flag which ensures that bounding boxes do not overflow the window."
]
},
@@ -707,12 +774,22 @@
"### Chip Classification - `ChipClassificationLabelSource`"
]
},
+ {
+ "cell_type": "raw",
+ "id": "a1d361c8-2acf-4c76-94d0-fe0cd2f04f67",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "The :class:`~chip_classification_label_source.ChipClassificationLabelSource` can do the following:"
+ ]
+ },
{
"cell_type": "markdown",
- "id": "8c2e2209-daad-499e-994e-631910be4cdc",
+ "id": "592f4cc5-fbe9-42a4-8201-3c92b257661c",
"metadata": {},
"source": [
- "The `ChipClassificationLabelSource` can do the following:\n",
"1. The trivial case: given a rectangular polygons (representing chips) with associated class IDs, allow querying the class ID for those chips.\n",
"2. The more interesting case: given polygons representing the members of the target class(es), infer the class ID for a window based on how much it overlaps with any label-polygon.\n",
"\n",
@@ -784,13 +861,11 @@
]
},
{
- "cell_type": "markdown",
- "id": "5af9be49-70e1-4479-8fca-eaf49d856b2f",
+ "cell_type": "raw",
+ "id": "6d0414b6-4f53-4154-8d91-9c7fe0701d6e",
"metadata": {},
"source": [
- "`ChipClassificationLabelSource` accepts a number of options for configuring the class ID inference behavior. These must be specified through the `ChipClassificationLabelSourceConfig`.\n",
- "\n",
- "See documentation for `ChipClassificationLabelSourceConfig` for details."
+ ":class:`~chip_classification_label_source.ChipClassificationLabelSource` accepts a number of options for configuring the class ID inference behavior. These must be specified through the :class:`~chip_classification_label_source_config.ChipClassificationLabelSourceConfig`."
]
},
{
@@ -1119,25 +1194,47 @@
]
},
{
- "cell_type": "markdown",
- "id": "921137c8-c804-4dd7-8485-88b99651dd45",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "166aa26e-507b-471e-a897-7979e17e4005",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "The `Labels` class is a source-agnostic, in-memory representation of the labels in a scene. \n",
+ ".. currentmodule:: rastervision.core.data\n",
"\n",
- "We can get all or a subset of a `LabelSource` as a `Labels` instance, by calling `LabelSource.get_labels()`. E.g.\n",
+ "The :class:`~label.labels.Labels` class is a source-agnostic, in-memory representation of the labels in a scene. \n",
"\n",
+ "We can get all or a subset of a :class:`~label_source.label_source.LabelSource` as a :class:`~label.labels.Labels` instance, by calling :meth:`LabelSource.get_labels() <label_source.label_source.LabelSource.get_labels>`. E.g."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "77290874-a1d8-483b-9253-5a01f602bc15",
+ "metadata": {},
+ "source": [
"```python\n",
"labels = label_source.get_labels(window)\n",
"```\n",
"\n",
- "Crucially, the predictions produced by a model can also be converted to `Labels` instance, allowing them to be compared agains the `Labels` from the `LabelSource` (i.e, the ground truth) to get performance metrics. This is essentially what `Evaluator` does.\n",
+ "Crucially, the predictions produced by a model can also be converted to `Labels` instance, allowing them to be compared agains the `Labels` from the `LabelSource` (i.e, the ground truth) to get performance metrics. This is essentially what `Evaluator` does."
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "fcd8c13d-20a0-4f23-8ecb-8e2908b3fc72",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core.data.label\n",
"\n",
"There is one for each of the three tasks:\n",
"\n",
- "- `ChipClassificationLabels`\n",
- "- `SemanticSegmentationLabels`\n",
- "- `ObjectDetectionLabels`"
+ "- :class:`~chip_classification_labels.ChipClassificationLabels`\n",
+ "- :class:`~semantic_segmentation_labels.SemanticSegmentationLabels`\n",
+ "- :class:`~object_detection_labels.ObjectDetectionLabels`\n"
]
}
],
@@ -1157,7 +1254,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.13"
+ "version": "3.9.10"
},
"vscode": {
"interpreter": {
diff --git a/docs/usage/tutorials/reading_raster_data.ipynb b/docs/usage/tutorials/reading_raster_data.ipynb
index eef655b31..ac273b57e 100644
--- a/docs/usage/tutorials/reading_raster_data.ipynb
+++ b/docs/usage/tutorials/reading_raster_data.ipynb
@@ -19,11 +19,16 @@
]
},
{
- "cell_type": "markdown",
- "id": "8fdeb64d-52e2-4788-86a1-13f437519fcb",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "149f8987-26c2-4bfa-ad22-c7cbc3d47b50",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "The `RasterSource` is Raster Vision's abstraction for windowed reading from a raster image. "
+ ".. currentmodule:: rastervision.core.data\n",
+ "\n",
+ "The :class:`~raster_source.raster_source.RasterSource` is Raster Vision's abstraction for windowed reading from a raster image. "
]
},
{
@@ -34,13 +39,22 @@
"---"
]
},
+ {
+ "cell_type": "raw",
+ "id": "49250498-e0a2-46ad-80cf-20a623b45a2a",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "One concrete implementation of it is the :class:`~raster_source.rasterio_source.RasterioSource` which is a wrapper around the `rasterio library <https://rasterio.readthedocs.io>`__ and allows reading from all file formats supported by it."
+ ]
+ },
{
"cell_type": "markdown",
- "id": "47b5824c-2b90-4509-87bc-36ae3ff20d14",
+ "id": "fd326886-f67a-4c70-a4fd-c7328042b61d",
"metadata": {},
"source": [
- "One concrete implementation of it is the `RasterioSource` which is a wrapper around the [rasterio library](https://rasterio.readthedocs.io/) and allows reading from all file formats supported by it.\n",
- "\n",
"We can create one from an image like shown below. `allow_streaming=True` allows us to take advantage of `rasterio`'s remote-file-reading capabilities; setting it to `False` will cause Raster Vision to download the image."
]
},
@@ -226,11 +240,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "3f1f2481-4098-412d-9e99-489a18408a92",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "3045d467-ce72-4816-92d1-02dba407f999",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "The fancy slicing is just syntactic-sugar for the `RasterioSource.get_chip()` method. The last call is internally translated to the following call to the `.get_chip()` method:"
+ "The fancy slicing is just syntactic-sugar for the :meth:`RasterioSource.get_chip() <raster_source.rasterio_source.RasterioSource.get_chip>` method. The last call is internally translated to the following call to the :meth:`~raster_source.rasterio_source.RasterioSource.get_chip` method:"
]
},
{
@@ -260,6 +277,17 @@
"chip.shape"
]
},
+ {
+ "cell_type": "raw",
+ "id": "e70e3334-d2e7-48dc-aae8-5526bd49ab02",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "Learn more about the handy :class:`Box class here <rastervision.core.box.Box>`."
+ ]
+ },
{
"cell_type": "markdown",
"id": "6accd016-0862-4305-9ae6-0572d6c95e0c",
@@ -277,21 +305,28 @@
]
},
{
- "cell_type": "markdown",
- "id": "e712696d-2174-403f-9b3e-ae65399a576b",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "a326a97c-dbb5-456f-9c7b-cbe0f3d5b809",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "`RasterSource`s accept a list of `RasterTransformer`s, all of which are automatically applied (in the order specified) to each chip sampled from that `RasterSource`.\n",
+ ":class:`RasterSources <raster_source.raster_source.RasterSource>` accept a list of :class:`RasterTransformers <raster_transformer.raster_transformer.RasterTransformer>`, all of which are automatically applied (in the order specified) to each chip sampled from that :class:`~raster_source.raster_source.RasterSource`.\n",
+ "\n",
+ ".. currentmodule:: rastervision.core.data.raster_transformer\n",
"\n",
"Below we'll look at two such `RasterTransformer`s:\n",
- "- `MinMaxTransformer`\n",
- "- `StatsTransformer`\n",
+ "\n",
+ "- :class:`~min_max_transformer.MinMaxTransformer`\n",
+ "- :class:`~stats_transformer.StatsTransformer`\n",
"\n",
"But Raster Vision also provides the following:\n",
- "- `CastTransformer`: type-cast chip.\n",
- "- `NanTransformer`: map NaN values to another value.\n",
- "- `ReclassTransformer`: map values to other values using a given mapping; most useful for modifying class IDs in semantic segmentation labels.\n",
- "- `RGBClassTrasnformer`: if your semantic segmentation labels are in the form of an RGB raster with different colors representing different classes, use this to map them to class IDs."
+ "\n",
+ "- :class:`~cast_transformer.CastTransformer`: type-cast chip.\n",
+ "- :class:`~nan_transformer.NanTransformer`: map NaN values to another value.\n",
+ "- :class:`~reclass_transformer.ReclassTransformer`: map values to other values using a given mapping; most useful for modifying class IDs in semantic segmentation labels.\n",
+ "- :class:`~rgb_class_transformer.RGBClassTransformer`: if your semantic segmentation labels are in the form of an RGB raster with different colors representing different classes, use this to map them to class IDs.\n"
]
},
{
@@ -311,11 +346,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "f5b96d09-d207-424f-899b-79626623380b",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "94fa17ba-821d-4dae-8bf7-c66d2bd4a51a",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "Above, we manually min-max normalized the `uint16` chip to 0-1. If we wanted this normalization to be automatically applied to all chips sampled from a `RasterSource`, we can simply attach a `MinMaxTransformer` to that `RasterSource`."
+ "Above, we manually min-max normalized the ``uint16`` chip to 0-1. If we wanted this normalization to be automatically applied to all chips sampled from a ``RasterSource``, we can simply attach a :class:`~min_max_transformer.MinMaxTransformer` to that ``RasterSource``."
]
},
{
@@ -384,11 +422,16 @@
]
},
{
- "cell_type": "markdown",
- "id": "1b9364a5-0041-4f83-b7a6-b552a72a3e99",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "b78ac5f5-413c-46c2-94d2-164dcb2e3295",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "Another useful `RasterTransformer` is the `StatsTransformer`. Unlike `MinMaxTransformer`, the `StatsTransformer` is able to deal with outlier values. It works by using channel means and standard deviations to convert values to z-scores and then clipping them to some number of standard deviations before scaling to 0-255 and converting to `uint8`.\n",
+ "Another useful ``RasterTransformer`` is the :class:`~stats_transformer.StatsTransformer`. \n",
+ "\n",
+ "Unlike `MinMaxTransformer <#MinMaxTransformer>`_, the :class:`~stats_transformer.StatsTransformer` is able to deal with outlier values. It works by using channel means and standard deviations to convert values to z-scores and then clipping them to some number of standard deviations before scaling to 0-255 and converting to ``uint8``.\n",
"\n",
"We can create and use one like so:"
]
@@ -755,14 +798,25 @@
"## Combining bands from different files using `MultiRasterSource`"
]
},
+ {
+ "cell_type": "raw",
+ "id": "703bf4a8-0e48-41f6-9906-3203e2d54fa9",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core.data\n",
+ "\n",
+ "Another common use case is combining bands from multiple sources. This can done using a :class:`~raster_source.multi_raster_source.MultiRasterSource`."
+ ]
+ },
{
"cell_type": "markdown",
- "id": "9bc69e7e-41c5-4d63-ba2c-12f98ef95dcf",
+ "id": "36555340-f28d-4ba5-8c02-3af3b648e8d6",
"metadata": {},
"source": [
- "Another common use case is combining bands from multiple sources.\n",
- "\n",
- "The following example combines RGB, SWIR, and SAR bands into a single 8-band `RasterSource`."
+ "The following example combines RGB, SWIR, and SAR bands into a single 8-band ``RasterSource``."
]
},
{
@@ -786,6 +840,14 @@
"uri_seninel_1_vv = 'https://radiantearth.blob.core.windows.net/mlhub/c2smsfloods/chips/e7d1917e-c069-45cf-a392-42e24aa2f4ac/s1/S1B_IW_GRDH_1SDV_20201020T164222_20201020T164247_023899_02D6C4_35D8_07680-00512/VV.tif'"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "d00bdf3d-89f7-47fa-89cf-e7be80e9c64c",
+ "metadata": {},
+ "source": [
+ "First, we create `RasterSource`s for each individual source."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 21,
@@ -818,6 +880,17 @@
"print('rs_seninel_1_vv', rs_seninel_1_vv.shape, rs_seninel_1_vv.dtype)"
]
},
+ {
+ "cell_type": "raw",
+ "id": "576a77c3-6a00-42cd-90ec-4a5fad2da739",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "Next, we combine them into a :class:`~raster_source.multi_raster_source.MultiRasterSource`."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 22,
@@ -1016,7 +1089,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.13"
+ "version": "3.9.10"
},
"vscode": {
"interpreter": {
diff --git a/docs/usage/tutorials/reading_vector_data.ipynb b/docs/usage/tutorials/reading_vector_data.ipynb
index 070c57b53..987720a83 100644
--- a/docs/usage/tutorials/reading_vector_data.ipynb
+++ b/docs/usage/tutorials/reading_vector_data.ipynb
@@ -9,11 +9,18 @@
]
},
{
- "cell_type": "markdown",
- "id": "35ca06d8-1c57-44af-bf98-b29db349a21e",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "eff50e02-7b30-4b4d-8fb5-1308077abb9c",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "The `VectorSource` is Raster Vision's abstraction for reading from a source of vector data. Besides reading the data, `VectorSource`s also convert geometries from map to pixel coordinates and perform some data cleaning such as removing empty geometries and splitting apart multi-part geometries (e.g. MultiPolygon etc.)."
+ ".. currentmodule:: rastervision.core.data\n",
+ "\n",
+ "The :class:`~vector_source.vector_source.VectorSource` is Raster Vision's abstraction for reading from a source of vector data. \n",
+ "\n",
+ "Besides reading the data, they can also convert geometries from map-coordinates to pixel-coordinates and perform some data cleaning such as removing empty geometries and splitting apart multi-part geometries (e.g. ``MultiPolygon`` etc.)."
]
},
{
@@ -25,11 +32,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "632fc382-a853-401a-9f64-169a85cbfba7",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "7702313b-77cb-43ed-861e-6f835349f946",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "One concrete implementation of it is the `GeoJSONVectorSource` which can read vector data from a GeoJSON file."
+ "One concrete implementation of it is the :class:`~vector_source.geojson_vector_source.GeoJSONVectorSource` which can read vector data from a GeoJSON file."
]
},
{
@@ -50,15 +60,18 @@
]
},
{
- "cell_type": "markdown",
- "id": "ce15abef-0154-4606-bc6b-d884aef84dc7",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "ffe72386-b0a1-4de6-877d-49d726876372",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "We can read data from a VectorSource in three different formats:\n",
+ "We can read data from a :class:`~vector_source.vector_source.VectorSource` in three different formats:\n",
"\n",
- "1. as GeoJSON dict (`vector_source.get_geojson()`)\n",
- "2. as Shapely geoms (`vector_source.get_geoms()`)\n",
- "3. as a GeoPandas `GeoDataFrame` (`vector_source.get_dataframe()`)\n",
+ "1. as GeoJSON dict (:meth:`~vector_source.vector_source.VectorSource.get_geojson`)\n",
+ "2. as Shapely geoms (:meth:`~vector_source.vector_source.VectorSource.get_geoms`)\n",
+ "3. as a GeoPandas :class:`~geopandas.GeoDataFrame` (:meth:`~vector_source.vector_source.VectorSource.get_dataframe`)\n",
"\n",
"Each of these is shown in the following cells."
]
@@ -504,11 +517,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "fe29caeb-0e2a-4be9-b140-310d96ff0793",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "8dd13d9f-d3c7-4f8b-93ef-d93d80c7ffe7",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "Just like we can tansform rasters by specifying a series of `RasterTransformer`s, we can specify `VectorTransformer`s to transform vector data."
+ "Just like we can tansform rasters by specifying a series of :class:`RasterTransformers <raster_transformer.raster_transformer.RasterTransformer>`, we can specify :class:`VectorTransformers <vector_transformer.vector_transformer.VectorTransformer>` to transform vector data."
]
},
{
@@ -527,13 +543,24 @@
"### Inferring class IDs for polygons"
]
},
+ {
+ "cell_type": "raw",
+ "id": "eb379d6d-aced-4c00-8112-870ae749131d",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core.data.vector_transformer\n",
+ "\n",
+ "One very important :class:`~vector_transformer.vector_transformer.VectorTransformer` is the :class:`~class_inference_transformer.ClassInferenceTransformer`."
+ ]
+ },
{
"cell_type": "markdown",
- "id": "9ed2afaf-3e4f-4b59-997d-20a4f934cbed",
+ "id": "cfb9e2be-fa0d-41fe-97ab-1d8e2d116fde",
"metadata": {},
"source": [
- "One very important `VectorTransformer` is the `ClassInferenceTransformer`.\n",
- "\n",
"When using vector data in machine learning, it is important that each polygon be labeled with an appropriate class ID. But often, your data will not have this property stored in the GeoJSON file.\n",
"\n",
"The `ClassInferenceTransformer` can automatically infer and attach a `class_id` to each polygon read from the `VectorSource`. It can\n",
@@ -673,11 +700,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "3ff9230a-6563-41f8-a77c-eb49382199df",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "b9fd25bc-b9b0-4e31-b95e-0264b058cb9f",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "`Point` and `LineString` geometries are not directly useable if doing, say, semantic segmentation. The cells below show an example of converting road geometries (given in the form of `LineString`s) into polygons."
+ "``Point`` and ``LineString`` geometries are not directly useable if doing, say, semantic segmentation. The cells below show an example of converting road geometries (given in the form of ``LineString``s) into polygons using the :class:`~buffer_transformer.BufferTransformer`."
]
},
{
@@ -824,13 +854,24 @@
"## Rasterizing vector data using `RasterizedSource`"
]
},
+ {
+ "cell_type": "raw",
+ "id": "9e349df7-22fa-4997-a8c1-845d479b5350",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core.data\n",
+ "\n",
+ "Suppose we have semantic segmentation labels in the form of polygons. To use them for training, we will first need to convert them into rasters. Raster Vision allows accomplishing this using the :class:`~raster_source.rasterized_source.RasterizedSource` class."
+ ]
+ },
{
"cell_type": "markdown",
- "id": "f35a4746-2052-4d8d-9ac1-3f9c4f3fb5e3",
+ "id": "e32dbfa8-180b-482c-b83e-24c556a13c14",
"metadata": {},
"source": [
- "Suppose we have semantic segmentation labels in the form of polygons. To use them for training, we will first need to convert them into rasters. Raster Vision allows accomplishing this using the `RasterizedSource` class.\n",
- "\n",
"The `RasterizedSource` is a `RasterSource` that reads data from a `VectorSource` (rather than an image file) and then converts it into rasters. It can be indexed like any other `RasterSource`."
]
},
@@ -922,7 +963,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3.7.9 ('base')",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -936,7 +977,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.9"
+ "version": "3.9.10"
},
"vscode": {
"interpreter": {
diff --git a/docs/usage/tutorials/sampling_training_data.ipynb b/docs/usage/tutorials/sampling_training_data.ipynb
index 471b40596..8e227dce6 100644
--- a/docs/usage/tutorials/sampling_training_data.ipynb
+++ b/docs/usage/tutorials/sampling_training_data.ipynb
@@ -10,22 +10,28 @@
},
{
"cell_type": "markdown",
- "id": "b4aed88a-6109-4149-8a54-613efab85611",
+ "id": "8a93c346",
"metadata": {},
"source": [
"## The `GeoDataset` class"
]
},
{
- "cell_type": "markdown",
- "id": "7dd4906d-6913-4cde-ac11-c76a077179bb",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "acd8e949-9b74-4233-8006-897a1e251bbf",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "The `GeoDataset` is a PyTorch-compatible `Dataset` implementation that allows sampling images and labels from a `Scene` (which is a combination of a `RasterSource` and a `LabelSource`).\n",
+ ".. currentmodule:: rastervision.pytorch_learner.dataset\n",
+ "\n",
+ "The :class:`~dataset.GeoDataset` is a PyTorch-compatible :class:`~torch.utils.data.Dataset` implementation that allows sampling images and labels from a `Scene <scenes_and_aois.ipynb>`_.\n",
"\n",
"It comes in two flavors:\n",
- "1. `SlidingWindowGeoDataset`\n",
- "2. `RandomWindowGeoDataset`\n",
+ "\n",
+ "1. :class:`~dataset.SlidingWindowGeoDataset`\n",
+ "2. :class:`~dataset.RandomWindowGeoDataset`\n",
"\n",
"Below we explore both in the context of semantic segmentation."
]
@@ -48,7 +54,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"id": "5a7a3ce6-abcb-4f6b-8ce3-0e21a46b2c7c",
"metadata": {},
"outputs": [],
@@ -86,16 +92,19 @@
]
},
{
- "cell_type": "markdown",
- "id": "ba859407",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "1851353c-5c48-4080-b69f-29a07f2e6361",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "The SlidingWindowGeoDataset allows reading the scene left-to-right, top-to-bottom, using a sliding window."
+ "The :class:`~dataset.SlidingWindowGeoDataset` allows reading the scene left-to-right, top-to-bottom, using a sliding window."
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 2,
"id": "877f5cf8-fb08-447d-b3ed-9d480b4463a7",
"metadata": {},
"outputs": [],
@@ -105,16 +114,19 @@
]
},
{
- "cell_type": "markdown",
- "id": "a57f6519-028d-4b7d-b48d-f6ba11f37126",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "56409a53-87fd-494c-83cb-b03b62c65b27",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "Here we make use of the convenience API, `GeoDataset.from_uris()`, but we can also use the normal constructor if we want to manually define the `RasterSource` and `LabelSource`."
+ "Here we make use of the convenience API, :meth:`~dataset.GeoDataset.from_uris()` (specifically, :meth:`~semantic_segmentation_dataset.SemanticSegmentationSlidingWindowGeoDataset.from_uris`), but we can also use the normal constructor if we want to manually define the `RasterSource <reading_raster_data.ipynb>`_ and `LabelSource <reading_labels.ipynb>`_."
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 3,
"id": "f9d43771-319f-4b80-b507-7c5df29501f1",
"metadata": {
"tags": []
@@ -124,8 +136,8 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-11-14 17:21:30:rastervision.core.data.raster_source.rasterio_source: WARNING - Raster block size (2, 1024) is too non-square. This can slow down reading. Consider re-tiling using GDAL.\n",
- "2022-11-14 17:21:30:rastervision.pipeline.file_system.utils: INFO - Using cached file /opt/data/tmp/cache/s3/spacenet-dataset/spacenet/SN7_buildings/train/L15-0331E-1257N_1327_3160_13/labels/global_monthly_2018_01_mosaic_L15-0331E-1257N_1327_3160_13_Buildings.geojson.\n"
+ "2022-12-02 12:39:05:rastervision.core.data.raster_source.rasterio_source: WARNING - Raster block size (2, 1024) is too non-square. This can slow down reading. Consider re-tiling using GDAL.\n",
+ "2022-12-02 12:39:05:rastervision.pipeline.file_system.utils: INFO - Using cached file /opt/data/tmp/cache/s3/spacenet-dataset/spacenet/SN7_buildings/train/L15-0331E-1257N_1327_3160_13/labels/global_monthly_2018_01_mosaic_L15-0331E-1257N_1327_3160_13_Buildings.geojson.\n"
]
}
],
@@ -163,7 +175,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 4,
"id": "a6325a6a-8cb6-4ee0-b7c3-fa1eac069d0f",
"metadata": {},
"outputs": [
@@ -173,7 +185,7 @@
"(torch.Size([3, 256, 256]), torch.Size([256, 256]))"
]
},
- "execution_count": 7,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -184,22 +196,25 @@
]
},
{
- "cell_type": "markdown",
- "id": "efe2fb6d",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "b0ebdccd-1acc-4191-bafa-49c4fd5d94ee",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "And then plot it using the `SemanticSegmentationVisualizer`:"
+ "And then plot it using the `SemanticSegmentationVisualizer <visualize_data_samples.ipynb>`_:"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 5,
"id": "04b4a84b-24fd-4190-8d5c-993ce3bbd422",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
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\n",
"text/plain": [
"<Figure size 600x300 with 2 Axes>"
]
@@ -211,7 +226,7 @@
"source": [
"viz = SemanticSegmentationVisualizer(\n",
" class_names=class_config.names, class_colors=class_config.colors)\n",
- "viz.plot_batch(x.unsqueeze(0), y.unsqueeze(0), show=True)\n"
+ "viz.plot_batch(x.unsqueeze(0), y.unsqueeze(0), show=True)"
]
},
{
@@ -230,7 +245,7 @@
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 800x800 with 1 Axes>"
]
@@ -244,57 +259,6 @@
"show_windows(img_full, ds.windows, title='Sliding windows')"
]
},
- {
- "cell_type": "markdown",
- "id": "d25a771f",
- "metadata": {},
- "source": [
- "Note the windows on the edges that extend beyond the extent and will mostly be blank when sampled. We can get rid of them by adjusting the `padding` param:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "f0db73fa-5d83-452b-941e-7bca1f201d11",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2022-09-13 12:52:17:rastervision.pipeline.file_system.utils: INFO - Using cached file /opt/data/tmp/cache/s3/spacenet-dataset/spacenet/SN7_buildings/train/L15-0331E-1257N_1327_3160_13/labels/global_monthly_2018_01_mosaic_L15-0331E-1257N_1327_3160_13_Buildings.geojson.\n"
- ]
- },
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",
- "text/plain": [
- "<Figure size 800x800 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "ds = SemanticSegmentationSlidingWindowGeoDataset.from_uris(\n",
- " class_config=class_config,\n",
- " image_uri=image_uri,\n",
- " label_vector_uri=label_uri,\n",
- " label_vector_default_class_id=class_config.get_class_id('building'),\n",
- " image_raster_source_kw=dict(allow_streaming=True),\n",
- " size=200,\n",
- " stride=200,\n",
- " transform=A.Resize(256, 256),\n",
- " padding=0\n",
- ")\n",
- "\n",
- "img_full = ds.scene.raster_source[:, :]\n",
- "show_windows(img_full, ds.windows, title='Sliding windows, no padding')"
- ]
- },
{
"cell_type": "markdown",
"id": "128a7240-b542-41a6-b8b5-b016dad4f112",
@@ -312,11 +276,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "ba859407",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "8871132d-8332-4f28-95a9-2989ce4dca78",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "The SlidingWindowGeoDataset allows reading the scene by sampling random window sizes and locations."
+ "The :class:`~dataset.RandomWindowGeoDataset` allows reading the scene by sampling random window sizes and locations."
]
},
{
@@ -331,11 +298,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "ba6713b9-06fc-446d-8fdf-785591aa195e",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "4c5a8567-3cdb-43c1-8a7d-b9bf9bfa16fc",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "Here we make use of the convenience API, `GeoDataset.from_uris()`, but we can also use the normal constructor if we want to manually define the `RasterSource` and `LabelSource`."
+ "As before, we make use of the convenience API, :meth:`~dataset.GeoDataset.from_uris()` (specifically, :meth:`~semantic_segmentation_dataset.SemanticSegmentationRandomWindowGeoDataset.from_uris`), but we can also use the normal constructor if we want to manually define the `RasterSource <reading_raster_data.ipynb>`_ and `LabelSource <reading_labels.ipynb>`_."
]
},
{
@@ -383,8 +353,11 @@
" label_vector_uri=label_uri,\n",
" label_vector_default_class_id=class_config.get_class_id('building'),\n",
" image_raster_source_kw=dict(allow_streaming=True),\n",
+ " # window sizes will randomly vary from 100x100 to 300x300\n",
" size_lims=(100, 300),\n",
+ " # resize chips to 256x256 before returning\n",
" out_size=256,\n",
+ " # allow windows to overflow the extent by 100 pixels\n",
" padding=100\n",
")\n",
"\n",
@@ -420,7 +393,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3.8.9 64-bit",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
diff --git a/docs/usage/tutorials/scenes_and_aois.ipynb b/docs/usage/tutorials/scenes_and_aois.ipynb
index 7671e50dc..0a4bc8a1e 100644
--- a/docs/usage/tutorials/scenes_and_aois.ipynb
+++ b/docs/usage/tutorials/scenes_and_aois.ipynb
@@ -24,7 +24,9 @@
"tags": []
},
"source": [
- "This tutorial introduces the :class:`~rastervision.core.data.scene.Scene` abstraction, which bundles them together. Additionally, it allows specifying one or more \"Areas of Interest\" (AOIs) if we are only interested in a subset of the scene."
+ ".. currentmodule:: rastervision.core.data\n",
+ "\n",
+ "This tutorial introduces the :class:`~scene.Scene` abstraction, which bundles them together. Additionally, it allows specifying one or more \"Areas of Interest\" (AOIs) if we are only interested in a subset of the scene."
]
},
{
@@ -55,11 +57,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "603479b4-3a81-40e9-9381-ec9618c23db9",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "88c3969a-c5c5-410b-b747-f3317d1083fd",
+ "metadata": {
+ "raw_mimetype": "custom",
+ "tags": []
+ },
"source": [
- "Define a `RasterSource`:"
+ "Define a :class:`~raster_source.raster_source.RasterSource`:"
]
},
{
@@ -75,11 +80,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "eb414969-52bf-4380-a406-d2e825d542fd",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "7a8d2e97-2194-49e4-bedc-6fb8427db095",
+ "metadata": {
+ "raw_mimetype": "custom",
+ "tags": []
+ },
"source": [
- "Define a `LabelSource`:"
+ "Define a :class:`~label_source.label_source.LabelSource`:"
]
},
{
@@ -128,7 +136,7 @@
"id": "6f4f6a4f-dddc-4aa4-8501-f9706bff21c6",
"metadata": {},
"source": [
- "Define AOI:"
+ "Define some AOI using polygons:"
]
},
{
@@ -151,7 +159,7 @@
"id": "cb437c76-a2f3-46d3-a63d-4b93e5ce4a0e",
"metadata": {},
"source": [
- "Visualize AOI:"
+ "Visualize the AOI:"
]
},
{
@@ -204,11 +212,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "79308b6c-0711-41ef-ab16-7a148e48c425",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "be3af5a4-87bc-4fc4-9adf-61a45ee846cb",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "Finally, define a `Scene`:"
+ "Finally, define a :class:`~scene.Scene`:"
]
},
{
@@ -262,12 +273,21 @@
},
{
"cell_type": "markdown",
- "id": "f17cb1c0-ae4a-45fe-94cd-f142921a8389",
+ "id": "be873c72-c787-42ac-bdb1-2c2b6e113b7f",
"metadata": {},
"source": [
- "Note that the `Scene` itself does not prevent you from reading windows outside the AOI. \n",
- "\n",
- "A simple check to make sure you're inside the AOI before reading is to use the `Box.within_aoi` method:"
+ "Note that the `Scene` itself does not prevent you from reading windows outside the AOI. "
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "dfa90bf8-6dd4-430c-aa7b-6cd295d67823",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "A simple check to make sure you're inside the AOI before reading is to use the :meth:`~rastervision.core.box.Box.within_aoi` method:"
]
},
{
@@ -337,11 +357,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "e130597f-1074-4665-a27f-94a7c404ea6a",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "f2cc5c38-d8e7-466c-a271-0193a8a0152c",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "`make_ss_scene` is for semantic segmentation scenes. There are also `make_cc_scene` and `make_od_scene` for chip classificaiton and object detection respectively."
+ ":func:`~utils.factory.make_ss_scene` is for creating semantic segmentation scenes. There is also :func:`~utils.factory.make_cc_scene` for chip classificaiton and :func:`~utils.factory.make_od_scene` for object detection."
]
}
],
@@ -361,7 +384,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.13"
+ "version": "3.9.10"
}
},
"nbformat": 4,
diff --git a/docs/usage/tutorials/train.ipynb b/docs/usage/tutorials/train.ipynb
index 3988fb266..405ea234a 100644
--- a/docs/usage/tutorials/train.ipynb
+++ b/docs/usage/tutorials/train.ipynb
@@ -9,11 +9,15 @@
]
},
{
- "cell_type": "markdown",
- "id": "aff5aa2c-567e-4e11-9e7c-fc33234734a1",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "78a22a3f-f343-4ac3-9965-44cd4e76e7d2",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "## Define `ClassConfig`"
+ "Define `ClassConfig <reading_labels.ipynb#ClassConfig>`_\n",
+ "--------------------------------------------------------"
]
},
{
@@ -31,12 +35,24 @@
" null_class='background')"
]
},
+ {
+ "cell_type": "raw",
+ "id": "ab09caa7-b904-42a4-b862-0ff013bb0ce8",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "Define training and validation `datasets <sampling_training_data.ipynb>`_\n",
+ "-------------------------------------------------------------------------"
+ ]
+ },
{
"cell_type": "markdown",
- "id": "841136bb-f47e-4867-b625-039a2a565624",
+ "id": "5045902e-7a14-48a0-b21d-307db2eb986f",
"metadata": {},
"source": [
- "## Define training and validation datasets"
+ "To keep things simple, we use one scene for training and one for validation. In a real workflow, we would normally use many more scenes."
]
},
{
@@ -80,11 +96,15 @@
]
},
{
- "cell_type": "markdown",
- "id": "2b9b76ff-61f2-497f-a46d-b311754ec476",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "ffa43877-243e-46b8-817f-31bbf65ba6c7",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "### Training dataset with random-window sampling and data augmentation"
+ "Training dataset with `random-window sampling <sampling_training_data.ipynb#RandomWindowGeoDataset>`_ and data augmentation\n",
+ "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
]
},
{
@@ -141,11 +161,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "5a2a5f2f-b0c2-46d4-87d3-dc99088bf496",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "5cede845-db9c-4b51-b0e5-6a4105b3dcfe",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "Visualize:"
+ "`Visualize <visualize_data_samples.ipynb>`_:"
]
},
{
@@ -171,11 +194,15 @@
]
},
{
- "cell_type": "markdown",
- "id": "01b73eb9-1a8b-4c6a-9971-189034df347a",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "56bd2d6b-9e75-4e21-b4ce-5b5990fc4420",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "### Validation dataset with sliding-window sampling and no data augmentation"
+ "Validation dataset with `sliding-window sampling <sampling_training_data.ipynb#SlidingWindowGeoDataset>`_ (and no data augmentation)\n",
+ "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
]
},
{
@@ -217,11 +244,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "4902c093-9947-454b-b4f1-6c4e63f7d0c3",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "b5af755b-7fb3-47b3-88dc-e20de8a34dcf",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "Visualize:"
+ "`Visualize <visualize_data_samples.ipynb>`_:"
]
},
{
@@ -300,11 +330,26 @@
]
},
{
- "cell_type": "markdown",
- "id": "45f1f872-e551-40b9-83b2-6780fac3f317",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "98b4b022-5d2e-4451-8a6b-46a505ed6828",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.pytorch_learner.learner_config"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "b0ba6b5c-0f88-4184-b88a-229e601eb203",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "### `DataConfig` - Configure data-related hyper-parameters"
+ ":class:`DataConfig` -- Configure data-related hyper-parameters\n",
+ "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
]
},
{
@@ -324,11 +369,15 @@
]
},
{
- "cell_type": "markdown",
- "id": "3a65ed66-04fc-44db-b3aa-d6ebd5713182",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "de886b77-4740-4b0f-beef-c253df1eb714",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "### `SolverConfig` - Configure the loss, optimizer, and scheduler(s)"
+ ":class:`SolverConfig` -- Configure the loss, optimizer, and scheduler(s)\n",
+ "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
]
},
{
@@ -348,11 +397,15 @@
]
},
{
- "cell_type": "markdown",
- "id": "f2c9f179-8b79-4eca-aa4a-5f8c743a4a4e",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "530dd8a3-d655-4778-aabb-91fef6adb99e",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "### `LearnerConfig` - Combine `DataConfig`, `SovlerConfig` (and optionally, `ModelConfig`)"
+ ":class:`LearnerConfig` -- Combine :class:`DataConfig`, :class:`SolverConfig` (and optionally, :class:`ModelConfig`)\n",
+ "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
]
},
{
@@ -368,11 +421,26 @@
]
},
{
- "cell_type": "markdown",
- "id": "d6879c5c-9ccf-4b41-9c8f-fd63c712a58b",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "12c08fc2-85af-42e9-b68c-4927fb44bc2c",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.pytorch_learner.learner"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "7e682ccd-5f05-44fe-8289-1a4543162306",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "## Initialize `Learner`"
+ "Initialize :class:`Learner`\n",
+ "---------------------------"
]
},
{
@@ -420,20 +488,17 @@
]
},
{
- "cell_type": "markdown",
- "id": "56277ecc-c099-4b61-ae73-c35b1408582d",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "aab1374e-0157-4e2d-abc1-49d423cd6613",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "<div class=\"alert alert-info\">\n",
- "\n",
- "Note\n",
+ ".. note::\n",
"\n",
- "<ul>\n",
- " <li>If running inside the Raster Vision docker image, you will need to pass `--tensorboard` to `docker/run` for this to work.</li>\n",
- " <li>If the dashboard doen't auto-reload, you can click the reload button onthe top-right.</li>\n",
- "</ul>\n",
- "\n",
- "</div>"
+ " - If running inside the Raster Vision docker image, you will need to pass `--tensorboard` to `docker/run` for this to work.\n",
+ " - If the dashboard doen't auto-reload, you can click the reload button on the top-right.\n"
]
},
{
@@ -473,11 +538,15 @@
]
},
{
- "cell_type": "markdown",
- "id": "f554d467-99c8-4d89-a486-3ab6c6d72a26",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "c05e6588-ddb9-457e-9605-7acebda90d21",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "## Train"
+ "Train -- :meth:`Learner.train`\n",
+ "------------------------------"
]
},
{
@@ -1027,11 +1096,15 @@
]
},
{
- "cell_type": "markdown",
- "id": "a515a663-4e62-4e30-b821-21c22aedde5e",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "38127c1f-53db-4218-97db-a58af203ea70",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "## Examine predictions"
+ "Examine predictions -- :meth:`Learner.plot_predictions`\n",
+ "-------------------------------------------------------"
]
},
{
@@ -1072,11 +1145,15 @@
]
},
{
- "cell_type": "markdown",
- "id": "2cb66486-7f47-42bb-853f-c805c62f43c0",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "d3a2bd4c-1582-44ae-bb14-ef476162928d",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "## Save as a model-bundle"
+ "Save as a model-bundle -- :meth:`Learner.save_model_bundle`\n",
+ "-----------------------------------------------------------"
]
},
{
@@ -1168,7 +1245,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.13"
+ "version": "3.9.10"
}
},
"nbformat": 4,
diff --git a/docs/usage/tutorials/visualize_data_samples.ipynb b/docs/usage/tutorials/visualize_data_samples.ipynb
index ccc5b90e8..8f5daab03 100644
--- a/docs/usage/tutorials/visualize_data_samples.ipynb
+++ b/docs/usage/tutorials/visualize_data_samples.ipynb
@@ -4,14 +4,34 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Plot samples from `Dataset`s using `Visualizer`s\n",
- "\n",
- "This notebook shows how to use `Visualizer` objects to plot image/label samples for computer vision PyTorch `Dataset`s. There are examples for semantic segmentation, object detection, and image classification. We use Raster Vision's `GeoDataset` functionality to read the data, but the `Visualizer` classes can be used with any images and labels as long as they are in the expected format."
+ "# Plot samples from `Dataset`s using `Visualizer`s"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.pytorch_learner.dataset.visualizer"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "This notebook shows how to use :class:`~visualizer.Visualizer` objects to plot image/label samples for computer vision PyTorch :class:`Datasets <torch.utils.data.Dataset>`. There are examples for `semantic segmentation <#Semantic-Segmentation----SemanticSegmentationVisualizer>`_, `object detection <#Object-Detection----ObjectDetectionVisualizer>`_, and `image classification <#Image-Classification----ClassificationVisualizer>`_. We use Raster Vision's `GeoDataset <sampling_training_data.ipynb#The-GeoDataset-class>`_ functionality to read the data, but the :class:`~visualizer.Visualizer` classes can be used with any images and labels as long as they are in the expected format."
]
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "tags": []
+ },
"source": [
"## Setup"
]
@@ -60,10 +80,14 @@
]
},
{
- "cell_type": "markdown",
- "metadata": {},
+ "cell_type": "raw",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "## Semantic Segmentation"
+ "Semantic Segmentation -- :class:`~semantic_segmentation_visualizer.SemanticSegmentationVisualizer`\n",
+ "--------------------------------------------------------------------------------------------------"
]
},
{
@@ -141,10 +165,14 @@
]
},
{
- "cell_type": "markdown",
- "metadata": {},
+ "cell_type": "raw",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "## Object Detection"
+ "Object Detection -- :class:`~object_detection_visualizer.ObjectDetectionVisualizer`\n",
+ "-----------------------------------------------------------------------------------"
]
},
{
@@ -189,10 +217,14 @@
]
},
{
- "cell_type": "markdown",
- "metadata": {},
+ "cell_type": "raw",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "## Image Classification"
+ "Image Classification -- :class:`~classification_visualizer.ClassificationVisualizer`\n",
+ "------------------------------------------------------------------------------------"
]
},
{
@@ -244,7 +276,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3.7.9 ('base')",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -258,9 +290,8 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.9"
+ "version": "3.9.10"
},
- "orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "6850b013e1f3bd5bc88fc148f23f814e4d2f79564e8ea88f16f3069ee54d6960"
@@ -268,5 +299,5 @@
}
},
"nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
}
diff --git a/rastervision_core/rastervision/core/data/class_config.py b/rastervision_core/rastervision/core/data/class_config.py
index cdc06baa5..a31146546 100644
--- a/rastervision_core/rastervision/core/data/class_config.py
+++ b/rastervision_core/rastervision/core/data/class_config.py
@@ -120,5 +120,6 @@ def __len__(self) -> int:
@property
def color_triples(self) -> List[Tuple[float, float, float]]:
+ """Class colors in a normalized form."""
color_triples = [normalize_color(c) for c in self.colors]
return color_triples
|
searxng__searxng-2081 | DuckDuckGo returning "access denied" errors
<!-- PLEASE FILL THESE FIELDS, IT REALLY HELPS THE MAINTAINERS OF SearXNG -->
**Version of SearXNG, commit number if you are using on master branch and stipulate if you forked SearXNG**
2023.01.06-b241015e
**How did you install SearXNG?**
searxng-docker
**What happened?**
DuckDuckGo started returning "access denied" error messages. Very similar to previous issue #1854
**How To Reproduce**
Enable DuckDuckGo and try to search anything.
**Expected behavior**
DDG results should return and no "Access Denied" error message should be displayed.
**Screenshots & Logs**
Error message in question:
- Exception: searx.exceptions.SearxEngineAccessDeniedException
- Parameter: HTTP error 403
- Filename: searx/search/processors/online.py:113
- Function: _send_http_request
- Code: response = req(params['url'], **request_args)
**Additional context**
It looks like it can be fixed by adding a HTTP `Referer` header to the request.
DuckDuckGo returning "access denied" errors
<!-- PLEASE FILL THESE FIELDS, IT REALLY HELPS THE MAINTAINERS OF SearXNG -->
**Version of SearXNG, commit number if you are using on master branch and stipulate if you forked SearXNG**
2023.01.06-b241015e
**How did you install SearXNG?**
searxng-docker
**What happened?**
DuckDuckGo started returning "access denied" error messages. Very similar to previous issue #1854
**How To Reproduce**
Enable DuckDuckGo and try to search anything.
**Expected behavior**
DDG results should return and no "Access Denied" error message should be displayed.
**Screenshots & Logs**
Error message in question:
- Exception: searx.exceptions.SearxEngineAccessDeniedException
- Parameter: HTTP error 403
- Filename: searx/search/processors/online.py:113
- Function: _send_http_request
- Code: response = req(params['url'], **request_args)
**Additional context**
It looks like it can be fixed by adding a HTTP `Referer` header to the request.
| [
{
"content": "# SPDX-License-Identifier: AGPL-3.0-or-later\n# lint: pylint\n\"\"\"DuckDuckGo Lite\n\"\"\"\n\nfrom json import loads\n\nfrom lxml.html import fromstring\n\nfrom searx.utils import (\n dict_subset,\n eval_xpath,\n eval_xpath_getindex,\n extract_text,\n match_language,\n)\nfrom searx... | [
{
"content": "# SPDX-License-Identifier: AGPL-3.0-or-later\n# lint: pylint\n\"\"\"DuckDuckGo Lite\n\"\"\"\n\nfrom json import loads\n\nfrom lxml.html import fromstring\n\nfrom searx.utils import (\n dict_subset,\n eval_xpath,\n eval_xpath_getindex,\n extract_text,\n match_language,\n)\nfrom searx... | diff --git a/searx/engines/duckduckgo.py b/searx/engines/duckduckgo.py
index 0d82002bff6..84198afc56a 100644
--- a/searx/engines/duckduckgo.py
+++ b/searx/engines/duckduckgo.py
@@ -73,6 +73,7 @@ def request(query, params):
# link again and again ..
params['headers']['Content-Type'] = 'application/x-www-form-urlencoded'
+ params['headers']['Referer'] = 'https://lite.duckduckgo.com/'
# initial page does not have an offset
if params['pageno'] == 2:
|
kivy__python-for-android-2180 | Issues introduced by PR #2113 (SDL2)
As said on Discord #dev channel yesterday, PR #2113 introduces a lot of blocking issues.
These are the results of the tests done by me, @AndreMiras and @opacam :
- `sdl2==2.0.10` have issues that have been solved by the SDL2 team, so it needs to be bumped to `2.0.12`.
- `sdl2==2.0.12` works but create freezes during runtime.
- These freezes are definitely related to the new `SDL_LockMutex` / `SDL_UnlockMutex` mechanism they added for concurrency issues.
- Commenting `SDL_LockMutex` on `Touch` related events fixes the freeze issue for non-fullscreen apps.
- On fullscreen apps, the patch it's also needed on `Resize, .. etc` events.
I'm providing an attached patch that fixes the issues on top of `2.0.12`, btw seems not a good idea to do that, so it needs some more investigation:
[disable_mutex.txt](https://github.com/kivy/python-for-android/files/4569870/disable_mutex.txt)
| [
{
"content": "from pythonforandroid.recipe import BootstrapNDKRecipe\nfrom pythonforandroid.toolchain import current_directory, shprint\nimport sh\n\n\nclass LibSDL2Recipe(BootstrapNDKRecipe):\n version = \"2.0.10\"\n url = \"https://www.libsdl.org/release/SDL2-{version}.zip\"\n md5sum = \"6b2e9a4a2fab... | [
{
"content": "from pythonforandroid.recipe import BootstrapNDKRecipe\nfrom pythonforandroid.toolchain import current_directory, shprint\nimport sh\n\n\nclass LibSDL2Recipe(BootstrapNDKRecipe):\n version = \"2.0.9\"\n url = \"https://www.libsdl.org/release/SDL2-{version}.tar.gz\"\n md5sum = 'f2ecfba915c... | diff --git a/pythonforandroid/bootstraps/sdl2/build/src/main/java/org/kivy/android/PythonActivity.java b/pythonforandroid/bootstraps/sdl2/build/src/main/java/org/kivy/android/PythonActivity.java
index 956c5f5b59..33d0855ef3 100644
--- a/pythonforandroid/bootstraps/sdl2/build/src/main/java/org/kivy/android/PythonActivity.java
+++ b/pythonforandroid/bootstraps/sdl2/build/src/main/java/org/kivy/android/PythonActivity.java
@@ -193,7 +193,7 @@ protected void onPostExecute(String result) {
mActivity.getPackageName(), PackageManager.GET_META_DATA).metaData;
PowerManager pm = (PowerManager) mActivity.getSystemService(Context.POWER_SERVICE);
- if (mActivity.mMetaData.getInt("wakelock") == 1) {
+ if ( mActivity.mMetaData.getInt("wakelock") == 1 ) {
mActivity.mWakeLock = pm.newWakeLock(PowerManager.SCREEN_BRIGHT_WAKE_LOCK, "Screen On");
mActivity.mWakeLock.acquire();
}
@@ -450,32 +450,35 @@ public void appConfirmedActive() {
public void considerLoadingScreenRemoval() {
if (loadingScreenRemovalTimer != null)
return;
- if (PythonActivity.mSingleton != null &&
- mAppConfirmedActive &&
- loadingScreenRemovalTimer == null) {
- Log.v(TAG, "loading screen timer Runnable() launched.");
- // Remove loading screen but with a delay.
- // (app can use p4a's android.loadingscreen module to
- // do it quicker if it wants to)
- TimerTask removalTask = new TimerTask() {
- @Override
- public void run() {
- // post a runnable to the handler
- runOnUiThread(new Runnable() {
+ runOnUiThread(new Runnable() {
+ public void run() {
+ if (((PythonActivity)PythonActivity.mSingleton).mAppConfirmedActive &&
+ loadingScreenRemovalTimer == null) {
+ // Remove loading screen but with a delay.
+ // (app can use p4a's android.loadingscreen module to
+ // do it quicker if it wants to)
+ // get a handler (call from main thread)
+ // this will run when timer elapses
+ TimerTask removalTask = new TimerTask() {
@Override
public void run() {
- Log.v(TAG, "loading screen timer Runnable() finished.");
- PythonActivity activity =
- ((PythonActivity)PythonActivity.mSingleton);
- if (activity != null)
- activity.removeLoadingScreen();
+ // post a runnable to the handler
+ runOnUiThread(new Runnable() {
+ @Override
+ public void run() {
+ PythonActivity activity =
+ ((PythonActivity)PythonActivity.mSingleton);
+ if (activity != null)
+ activity.removeLoadingScreen();
+ }
+ });
}
- });
+ };
+ loadingScreenRemovalTimer = new Timer();
+ loadingScreenRemovalTimer.schedule(removalTask, 5000);
}
- };
- loadingScreenRemovalTimer = new Timer();
- loadingScreenRemovalTimer.schedule(removalTask, 5000);
- }
+ }
+ });
}
public void removeLoadingScreen() {
@@ -586,30 +589,14 @@ protected void onResume() {
if (this.mWakeLock != null) {
this.mWakeLock.acquire();
}
- Log.v(TAG, "onResume(), mSDLThread exists yet: " + (mSDLThread != null));
+ Log.v(TAG, "onResume()");
try {
super.onResume();
- if (mSDLThread == null && !mIsResumedCalled) {
- // Ok so SDL2's onStart() usually launches the native code.
- // However, this may fail if native libs aren't loaded yet at that point
- // (due ot our loading screen) so we may need to manually trigger this,
- // otherwise code would only launch by leaving & re-entering the app:
- Log.v(TAG, "Loading screen workaround: triggering native resume");
- if (mSDLThread == null && mCurrentNativeState == NativeState.RESUMED) {
- // Force a state change so SDL2 doesn't just ignore the resume:
- mCurrentNativeState = NativeState.PAUSED;
- }
- resumeNativeThread(); // native resume to call native code
- }
} catch (UnsatisfiedLinkError e) {
// Catch resume while still in loading screen failing to
// call native function (since it's not yet loaded)
- Log.v(TAG, "failed to call native onResume() because libs " +
- "aren't loaded yet. this is expected to happen");
}
considerLoadingScreenRemoval();
- Log.v(TAG, "onResume() done in PythonActivity, " +
- "mSDLThread exists yet: " + (mSDLThread != null));
}
@Override
@@ -619,7 +606,6 @@ public void onWindowFocusChanged(boolean hasFocus) {
} catch (UnsatisfiedLinkError e) {
// Catch window focus while still in loading screen failing to
// call native function (since it's not yet loaded)
- return; // no point in barging further
}
considerLoadingScreenRemoval();
}
diff --git a/pythonforandroid/bootstraps/sdl2/build/src/patches/SDLActivity.java.patch b/pythonforandroid/bootstraps/sdl2/build/src/patches/SDLActivity.java.patch
index 90daa10eea..27b97a7fbb 100644
--- a/pythonforandroid/bootstraps/sdl2/build/src/patches/SDLActivity.java.patch
+++ b/pythonforandroid/bootstraps/sdl2/build/src/patches/SDLActivity.java.patch
@@ -1,13 +1,12 @@
--- a/src/main/java/org/libsdl/app/SDLActivity.java
+++ b/src/main/java/org/libsdl/app/SDLActivity.java
-@@ -196,6 +196,16 @@ public class SDLActivity extends Activity implements View.OnSystemUiVisibilityCh
+@@ -196,6 +196,15 @@ public class SDLActivity extends Activity implements View.OnSystemUiVisibilityCh
Log.v(TAG, "onCreate()");
super.onCreate(savedInstanceState);
++ SDLActivity.initialize();
+ // So we can call stuff from static callbacks
+ mSingleton = this;
-+
-+ SDLActivity.initialize();
+ }
+
+ // We don't do this in onCreate because we unpack and load the app data on a thread
@@ -17,16 +16,6 @@
// Load shared libraries
String errorMsgBrokenLib = "";
try {
-@@ -508,7 +508,8 @@ public class SDLActivity extends Activity implements View.OnSystemUiVisibilityCh
- }
-
- // Try a transition to resumed state
-- if (mNextNativeState == NativeState.RESUMED) {
-+ // python-for-android: we delay finishLoad() -> mSurface can be null!
-+ if (mNextNativeState == NativeState.RESUMED && mSurface != null) {
- if (mSurface.mIsSurfaceReady && mHasFocus && mIsResumedCalled) {
- if (mSDLThread == null) {
- // This is the entry point to the C app.
@@ -639,7 +648,7 @@ public class SDLActivity extends Activity implements View.OnSystemUiVisibilityCh
Handler commandHandler = new SDLCommandHandler();
@@ -58,11 +47,28 @@
// APK expansion files support
/** com.android.vending.expansion.zipfile.ZipResourceFile object or null. */
-@@ -1475,5 +1475,7 @@ class SDLMain implements Runnable {
+@@ -1341,14 +1366,13 @@ public class SDLActivity extends Activity implements View.OnSystemUiVisibilityCh
+ };
+
+ public void onSystemUiVisibilityChange(int visibility) {
+- if (SDLActivity.mFullscreenModeActive && (visibility & View.SYSTEM_UI_FLAG_FULLSCREEN) == 0 || (visibility & View.SYSTEM_UI_FLAG_HIDE_NAVIGATION) == 0) {
+-
++ // SDL2 BUGFIX (see sdl bug #4424 ) - REMOVE WHEN FIXED IN UPSTREAM !!
++ if (SDLActivity.mFullscreenModeActive && ((visibility & View.SYSTEM_UI_FLAG_FULLSCREEN) == 0 || (visibility & View.SYSTEM_UI_FLAG_HIDE_NAVIGATION) == 0)) {
+ Handler handler = getWindow().getDecorView().getHandler();
+ if (handler != null) {
+ handler.removeCallbacks(rehideSystemUi); // Prevent a hide loop.
+ handler.postDelayed(rehideSystemUi, 2000);
+ }
+-
+ }
+ }
+
+@@ -1475,6 +1499,7 @@ class SDLMain implements Runnable {
+ String[] arguments = SDLActivity.mSingleton.getArguments();
+
Log.v("SDL", "Running main function " + function + " from library " + library);
-
+ SDLActivity.mSingleton.appConfirmedActive();
-+ Log.v("SDL", "(python-for-android: appConfirmedActive() was called)");
SDLActivity.nativeRunMain(library, function, arguments);
Log.v("SDL", "Finished main function");
diff --git a/pythonforandroid/recipes/sdl2/__init__.py b/pythonforandroid/recipes/sdl2/__init__.py
index ddf373c27b..830e9dde44 100644
--- a/pythonforandroid/recipes/sdl2/__init__.py
+++ b/pythonforandroid/recipes/sdl2/__init__.py
@@ -4,9 +4,9 @@
class LibSDL2Recipe(BootstrapNDKRecipe):
- version = "2.0.10"
- url = "https://www.libsdl.org/release/SDL2-{version}.zip"
- md5sum = "6b2e9a4a2faba4ff277062cf669724f4"
+ version = "2.0.9"
+ url = "https://www.libsdl.org/release/SDL2-{version}.tar.gz"
+ md5sum = 'f2ecfba915c54f7200f504d8b48a5dfe'
dir_name = 'SDL'
|
keras-team__keras-677 | Python 3 compatibility problem with Image loading
Loading an Image using the `load_img` results in an error.
```
Traceback (most recent call last):
File "keras/autoencoder.py", line 45, in <module>
X_train, Y_train, X_test, Y_test, nb_classes = io.load_images(join(DATA_DIR, 'dataset0'))
File "/home/jnphilipp/Documents/cnn/hieroglyphs/keras/utils/io.py", line 27, in load_images
X_train.append(img_to_array(load_img(picture, True)))
File "/home/jnphilipp/.local/lib/python3.4/site-packages/Keras-0.1.2-py3.4.egg/keras/preprocessing/image.py", line 107, in load_img
File "/home/jnphilipp/.local/lib/python3.4/site-packages/PIL/Image.py", line 2330, in open
% (filename if filename else fp))
OSError: cannot identify image file <_io.TextIOWrapper name='/home/jnphilipp/Documents/cnn/hieroglyphs/data/dataset0/train/P1_train0.png' mode='r' encoding='ISO-8859-1'>
```
| [
{
"content": "from __future__ import absolute_import\n\nimport numpy as np\nimport re\nfrom scipy import ndimage\nfrom scipy import linalg\n\nfrom os import listdir\nfrom os.path import isfile, join\nimport random, math\nfrom six.moves import range\n\n'''\n Fairly basic set of tools for realtime data augment... | [
{
"content": "from __future__ import absolute_import\n\nimport numpy as np\nimport re\nfrom scipy import ndimage\nfrom scipy import linalg\n\nfrom os import listdir\nfrom os.path import isfile, join\nimport random, math\nfrom six.moves import range\n\n'''\n Fairly basic set of tools for realtime data augment... | diff --git a/keras/preprocessing/image.py b/keras/preprocessing/image.py
index 5b64a588ad9e..ad9794a496cc 100644
--- a/keras/preprocessing/image.py
+++ b/keras/preprocessing/image.py
@@ -104,7 +104,7 @@ def img_to_array(img):
def load_img(path, grayscale=False):
from PIL import Image
- img = Image.open(open(path))
+ img = Image.open(path)
if grayscale:
img = img.convert('L')
else: # Assure 3 channel even when loaded image is grayscale
|
pyro-ppl__numpyro-737 | Possible error in the validation of a Categorical distribution
I am getting an error when I try to run the following code. The code just sample from a categorical distribution using the defined probabilities.
```python
import numpyro
import numpyro.distributions as dist
import jax.numpy as jnp
numpyro.enable_validation(True)
def model():
probs = jnp.array([0.5, 0.5, 0.])
c = numpyro.sample('c', dist.Categorical(probs=probs))
return c
with numpyro.handlers.seed(rng_seed=54):
print(model())
```
```
ValueError Traceback (most recent call last)
<ipython-input-1-fc7fe60e083b> in <module>
10
11 with numpyro.handlers.seed(rng_seed=54):
---> 12 print(model())
<ipython-input-1-fc7fe60e083b> in model()
6 def model():
7 probs = jnp.array([0.5, 0.5, 0.])
----> 8 c = numpyro.sample('c', dist.Categorical(probs=probs))
9 return c
10
~/miniconda3/envs/numpyro_test/lib/python3.8/site-packages/numpyro/distributions/discrete.py in Categorical(probs, logits, validate_args)
348 def Categorical(probs=None, logits=None, validate_args=None):
349 if probs is not None:
--> 350 return CategoricalProbs(probs, validate_args=validate_args)
351 elif logits is not None:
352 return CategoricalLogits(logits, validate_args=validate_args)
~/miniconda3/envs/numpyro_test/lib/python3.8/site-packages/numpyro/distributions/discrete.py in __init__(self, probs, validate_args)
265 raise ValueError("`probs` parameter must be at least one-dimensional.")
266 self.probs = probs
--> 267 super(CategoricalProbs, self).__init__(batch_shape=jnp.shape(self.probs)[:-1],
268 validate_args=validate_args)
269
~/miniconda3/envs/numpyro_test/lib/python3.8/site-packages/numpyro/distributions/distribution.py in __init__(self, batch_shape, event_shape, validate_args)
142 if not_jax_tracer(is_valid):
143 if not is_valid:
--> 144 raise ValueError("The parameter {} has invalid values".format(param))
145 super(Distribution, self).__init__()
146
ValueError: The parameter probs has invalid values
```
I think the problem is caused by the validation because If I restart my kernel and comment the line ```numpyro.enable_validation(True)``` the code will run without problem. It will print 0 in my case.
If I write a similar code in Pyro with the validation enabled, I do not get an error.
```python
import torch
import pyro
import pyro.distributions as dist
pyro.enable_validation(True)
pyro.set_rng_seed(54)
def model():
probs = torch.tensor([0.5, 0.5, 0.])
c = pyro.sample('c', dist.Categorical(probs=probs))
return c
print(model())
```
I am using Python 3.8.5, Pyro 1.4.0 and NumPyro 0.3.0 with Ubuntu. Happy to help with what I can.
| [
{
"content": "# Copyright Contributors to the Pyro project.\n# SPDX-License-Identifier: Apache-2.0\n\n# The implementation follows the design in PyTorch: torch.distributions.constraints.py\n#\n# Copyright (c) 2016- Facebook, Inc (Adam Paszke)\n# Copyright (c) 2014- Facebook, Inc (S... | [
{
"content": "# Copyright Contributors to the Pyro project.\n# SPDX-License-Identifier: Apache-2.0\n\n# The implementation follows the design in PyTorch: torch.distributions.constraints.py\n#\n# Copyright (c) 2016- Facebook, Inc (Adam Paszke)\n# Copyright (c) 2014- Facebook, Inc (S... | diff --git a/numpyro/distributions/constraints.py b/numpyro/distributions/constraints.py
index 91b8cf008..625f982fd 100644
--- a/numpyro/distributions/constraints.py
+++ b/numpyro/distributions/constraints.py
@@ -192,7 +192,7 @@ def __call__(self, x):
class _Simplex(Constraint):
def __call__(self, x):
x_sum = jnp.sum(x, axis=-1)
- return jnp.all(x > 0, axis=-1) & (x_sum < 1 + 1e-6) & (x_sum > 1 - 1e-6)
+ return jnp.all(x >= 0, axis=-1) & (x_sum < 1 + 1e-6) & (x_sum > 1 - 1e-6)
# TODO: Make types consistent
|
pytorch__ignite-2081 | DeterministicEngine rng state on cuda if loaded checkpoint on cuda
## 🐛 Bug description
Using `engine` as `DeterministicEngine`, loading on cuda can lead to the following issue:
```
File "/home/machine/.clearml/venvs-builds/3.6/lib/python3.6/site-packages/ignite/engine/engine.py", line 702, in run
return self._internal_run()
File "/home/machine/.clearml/venvs-builds/3.6/lib/python3.6/site-packages/ignite/engine/engine.py", line 775, in _internal_run
self._handle_exception(e)
File "/home/machine/.clearml/venvs-builds/3.6/lib/python3.6/site-packages/ignite/engine/engine.py", line 469, in _handle_exception
raise e
File "/home/machine/.clearml/venvs-builds/3.6/lib/python3.6/site-packages/ignite/engine/engine.py", line 743, in _internal_run
self._setup_engine()
File "/home/machine/.clearml/venvs-builds/3.6/lib/python3.6/site-packages/ignite/engine/deterministic.py", line 238, in _setup_engine
_set_rng_states(rng_states)
File "/home/machine/.clearml/venvs-builds/3.6/lib/python3.6/site-packages/ignite/engine/deterministic.py", line 100, in _set_rng_states
torch.set_rng_state(rng_states[1])
File "/home/machine/miniconda3/envs/py36/lib/python3.6/site-packages/torch/random.py", line 14, in set_rng_state
default_generator.set_state(new_state)
TypeError: expected a torch.ByteTensor, but got torch.cuda.ByteTensor
```
Code:
```
to_load = {
'state_dict': model,
'optimizer': optimizer,
'lr_scheduler': lr_scheduler,
'train_phase': engine
}
checkpoint = torch.load(checkpoint_fp, map_location="cuda:0")
Checkpoint.load_objects(to_load=to_load, checkpoint=checkpoint)
```
To fix the issue, we have to make sure that rng tensor is on cpu (otherwise put to cpu):
https://github.com/pytorch/ignite/blob/b49a82a8fb3339f1442752a0dbf2ecd6f15cb1fa/ignite/engine/deterministic.py#L238-L240
and add appropriate test.
### Workaround
- Load on cpu
```diff
- checkpoint = torch.load(checkpoint_fp, map_location="cuda:0")
+ checkpoint = torch.load(checkpoint_fp, map_location="cpu")
```
## Environment
- PyTorch Version (e.g., 1.4): 1.7.1
- Ignite Version (e.g., 0.3.0): 0.4.4
- OS (e.g., Linux):
- How you installed Ignite (`conda`, `pip`, source):
- Python version:
- Any other relevant information:
Thanks @H4dr1en for reporting.
| [
{
"content": "import random\nimport warnings\nfrom collections import OrderedDict\nfrom functools import wraps\nfrom typing import Any, Callable, Generator, Iterator, List, Optional\n\nimport torch\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data.sampler import BatchSampler\n\nfrom ignite.engine.... | [
{
"content": "import random\nimport warnings\nfrom collections import OrderedDict\nfrom functools import wraps\nfrom typing import Any, Callable, Generator, Iterator, List, Optional\n\nimport torch\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data.sampler import BatchSampler\n\nfrom ignite.engine.... | diff --git a/ignite/engine/deterministic.py b/ignite/engine/deterministic.py
index bca969cb3f02..79cd39ab73e3 100644
--- a/ignite/engine/deterministic.py
+++ b/ignite/engine/deterministic.py
@@ -98,6 +98,10 @@ def _get_rng_states() -> List[Any]:
def _set_rng_states(rng_states: List[Any]) -> None:
random.setstate(rng_states[0])
+
+ if "cpu" not in rng_states[1].device.type:
+ rng_states[1] = rng_states[1].cpu()
+
torch.set_rng_state(rng_states[1])
try:
import numpy as np
diff --git a/tests/ignite/engine/test_deterministic.py b/tests/ignite/engine/test_deterministic.py
index f7a581ba4343..9f62ed9502e4 100644
--- a/tests/ignite/engine/test_deterministic.py
+++ b/tests/ignite/engine/test_deterministic.py
@@ -15,6 +15,7 @@
from ignite.engine.deterministic import (
DeterministicEngine,
ReproducibleBatchSampler,
+ _set_rng_states,
keep_random_state,
update_dataloader,
)
@@ -885,3 +886,12 @@ def test_dataloader_no_dataset_kind():
dataloader = OldDataLoader(dataloader)
engine.run(dataloader)
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU")
+def test__set_rng_states_cuda():
+ # Checks https://github.com/pytorch/ignite/issues/2076
+
+ rng_states = [random.getstate(), torch.get_rng_state().cuda(), np.random.get_state()]
+ _set_rng_states(rng_states)
+ assert rng_states[1].device.type == "cpu"
|
kivy__kivy-7038 | Documentation issue with on_ref_press
In [kivy/uix/label.py L1008](https://github.com/kivy/kivy/blob/master/kivy/uix/label.py#L1008), there is a documentation issue with on_ref_press.
`widget.on_ref_press(print_it)` should be `widget.bind(on_ref_press=print_it)`.
| [
{
"content": "'''Label\n=====\n\n.. image:: images/label.png\n :align: right\n\nThe :class:`Label` widget is for rendering text. It supports ascii and unicode\nstrings::\n\n # hello world text\n l = Label(text='Hello world')\n\n # unicode text; can only display glyphs that are available in the font\... | [
{
"content": "'''Label\n=====\n\n.. image:: images/label.png\n :align: right\n\nThe :class:`Label` widget is for rendering text. It supports ascii and unicode\nstrings::\n\n # hello world text\n l = Label(text='Hello world')\n\n # unicode text; can only display glyphs that are available in the font\... | diff --git a/kivy/uix/label.py b/kivy/uix/label.py
index ea9181a3c8..45f764ac05 100644
--- a/kivy/uix/label.py
+++ b/kivy/uix/label.py
@@ -1005,7 +1005,7 @@ def on_ref_press(self, ref):
def print_it(instance, value):
print('User click on', value)
widget = Label(text='Hello [ref=world]World[/ref]', markup=True)
- widget.on_ref_press(print_it)
+ widget.bind(on_ref_press=print_it)
.. note::
|
ansible__awx-10108 | tower_workflow_job_template not changing ask_limit_on_launch
<!-- Issues are for **concrete, actionable bugs and feature requests** only - if you're just asking for debugging help or technical support, please use:
- http://webchat.freenode.net/?channels=ansible-awx
- https://groups.google.com/forum/#!forum/awx-project
We have to limit this because of limited volunteer time to respond to issues! -->
##### ISSUE TYPE
- Bug Report
##### SUMMARY
When using tower_workflow_job_template to change ask_limit_on_launch the module is reporting OK and does not do the change.
##### ENVIRONMENT
* AWX version: 15.0.1
* AWX install method: docker on linux
* Ansible version: 2.10
* Operating System: SLES
* Web Browser: Chrome
##### STEPS TO REPRODUCE
1. Create a workflow template and set ask_limit_on_launch to true
2. Use the tower_workflow_job_template module to change ask_limit_on_launch to false
##### EXPECTED RESULTS
- Module should report change
- Workflow template should have ask_limit_on_launch set to false
##### ACTUAL RESULTS
- Module reports OK
- Workflow template have ask_limit_on_launch set to true
##### ADDITIONAL INFORMATION
I am using the following task:
```yaml
- name: Create a workflow job template
awx.awx.tower_workflow_job_template:
name: "{{ item.name }}"
description: "{{ item.description }}"
organization: "{{ item.organization }}"
survey_enabled: "{{ item.survey_enabled|default('no') }}"
ask_limit_on_launch: "{{ item.ask_limit_on_launch|default('no') }}"
notification_templates_success: "{{ item.notification_templates_success|default(omit) }}"
notification_templates_error: "{{ item.notification_templates_error|default(omit) }}"
tower_host: "{{ tower_host }}"
tower_username: "{{ tower_username }}"
tower_password: "{{ tower_password }}"
validate_certs: "{{ validate_certs }}"
loop: "{{ workflow_templates }}"
```
with the following input:
```yaml
workflow_templates:
- name: "Update the inventory"
description: "Update the inventory"
organization: "myOrg"
notification_templates_success:
- "Mail"
notification_templates_error:
- "Mail"
```
| [
{
"content": "#!/usr/bin/python\n# coding: utf-8 -*-\n\n\n# (c) 2020, John Westcott IV <john.westcott.iv@redhat.com>\n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\nfrom __future__ import absolute_import, division, print_function\n\n__metaclass__ = type\n\n\nANSI... | [
{
"content": "#!/usr/bin/python\n# coding: utf-8 -*-\n\n\n# (c) 2020, John Westcott IV <john.westcott.iv@redhat.com>\n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\nfrom __future__ import absolute_import, division, print_function\n\n__metaclass__ = type\n\n\nANSI... | diff --git a/awx_collection/plugins/modules/workflow_job_template.py b/awx_collection/plugins/modules/workflow_job_template.py
index 7fc6b66ccad1..d5686a66aa42 100644
--- a/awx_collection/plugins/modules/workflow_job_template.py
+++ b/awx_collection/plugins/modules/workflow_job_template.py
@@ -751,7 +751,7 @@ def main():
'webhook_service',
):
field_val = module.params.get(field_name)
- if field_val:
+ if field_val is not None:
new_fields[field_name] = field_val
if 'extra_vars' in new_fields:
diff --git a/awx_collection/tests/integration/targets/workflow_job_template/tasks/main.yml b/awx_collection/tests/integration/targets/workflow_job_template/tasks/main.yml
index 527481281ba3..87fd84ab1414 100644
--- a/awx_collection/tests/integration/targets/workflow_job_template/tasks/main.yml
+++ b/awx_collection/tests/integration/targets/workflow_job_template/tasks/main.yml
@@ -159,12 +159,30 @@
extra_vars: {'foo': 'bar', 'another-foo': {'barz': 'bar2'}}
labels:
- "{{ lab1 }}"
+ ask_inventory_on_launch: true
+ ask_scm_branch_on_launch: true
+ ask_limit_on_launch: true
+ ask_variables_on_launch: true
register: result
- assert:
that:
- "result is changed"
+# Turn off ask_ * settings to test that the issue/10057 has been fixed
+- name: Turn ask_* settings OFF
+ tower_workflow_job_template:
+ name: "{{ wfjt_name }}"
+ ask_inventory_on_launch: false
+ ask_scm_branch_on_launch: false
+ ask_limit_on_launch: false
+ ask_variables_on_launch: false
+ state: present
+
+- assert:
+ that:
+ - "result is changed"
+
# Node actions do what this schema command used to do
# schema: [{"success": [{"job_template": "{{ jt1_name }}"}], "job_template": "{{ jt2_name }}"}]
- name: Create leaf node
|
cloud-custodian__cloud-custodian-8120 | Wafv2 logging error when using cloudtrail mode
### Describe the bug
When my wafv2 logging policy runs after disabling logging I receive an error on a cloudtrail policy when using the DeleteLoggingConfiguration event.
### What did you expect to happen?
I expected the policy to match my resource.
### Cloud Provider
Amazon Web Services (AWS)
### Cloud Custodian version and dependency information
```shell
custodian version --debug
Please copy/paste the following info along with any bug reports:
Custodian: 0.9.21
Python: 3.8.0 (v3.8.0:fa919fdf25, Oct 14 2019, 10:23:27)
[Clang 6.0 (clang-600.0.57)]
Platform: posix.uname_result(sysname='Darwin', nodename='kristen-MacBook-Pro', release='21.4.0', version='Darwin Kernel Version 21.4.0: Mon Feb 21 20:35:58 PST 2022; root:xnu-8020.101.4~2/RELEASE_ARM64_T6000', machine='x86_64')
Using venv: True
Docker: False
Installed:
argcomplete==2.0.0
attrs==22.1.0
boto3==1.26.30
botocore==1.29.30
docutils==0.17.1
importlib-metadata==4.13.0
importlib-resources==5.10.1
jmespath==1.0.1
jsonschema==4.17.3
pkgutil-resolve-name==1.3.10
pyrsistent==0.19.2
python-dateutil==2.8.2
pyyaml==6.0
s3transfer==0.6.0
six==1.16.0
tabulate==0.8.10
urllib3==1.26.13
zipp==3.11.0
```
### Policy
```shell
Policy example:
- name: wafv2-log-testing
resource: aws.wafv2
mode:
role: arn:aws:iam::testing
type: cloudtrail
events:
- event: DeleteLoggingConfiguration
ids: requestParameters.resourceArn
source: wafv2.amazonaws.com
filters:
- not:
- type: logging
key: ResourceArn
value: present
```
### Relevant log/traceback output
```shell
Error when policy runs:
[ERROR] 2023-01-06T18:48:11.706Z 163a02a3-69d6-4d43-a307-365ddcb8ead7 error during policy executionTraceback (most recent call last): File "/var/task/c7n/handler.py", line 165, in dispatch_event p.push(event, context) File "/var/task/c7n/policy.py", line 1288, in push return mode.run(event, lambda_ctx) File "/var/task/c7n/policy.py", line 487, in run resources = self.resolve_resources(event) File "/var/task/c7n/policy.py", line 691, in resolve_resources return super().resolve_resources(event) File "/var/task/c7n/policy.py", line 469, in resolve_resources resources = self.policy.resource_manager.get_resources(resource_ids) File "/var/task/c7n/query.py", line 576, in get_resources resources = self.source.get_resources(ids) File "/var/task/c7n/query.py", line 227, in get_resources return self.query.get(self.manager, ids) File "/var/task/c7n/query.py", line 100, in get resources = self.filter(resource_manager, **params) File "/var/task/c7n/query.py", line 79, in filter return self._invoke_client_enum( File "/var/task/c7n/query.py", line 60, in _invoke_client_enum data = op(**params) File "/var/runtime/botocore/client.py", line 391, in _api_call return self._make_api_call(operation_name, kwargs) File "/var/runtime/botocore/client.py", line 691, in _make_api_call request_dict = self._convert_to_request_dict( File "/var/runtime/botocore/client.py", line 739, in _convert_to_request_dict request_dict = self._serializer.serialize_to_request( File "/var/runtime/botocore/validate.py", line 360, in serialize_to_request raise ParamValidationError(report=report.generate_report())botocore.exceptions.ParamValidationError: Parameter validation failed:Missing required parameter in input: "Scope" | [ERROR] 2023-01-06T18:48:11.706Z 163a02a3-69d6-4d43-a307-365ddcb8ead7 error during policy execution Traceback (most recent call last): File "/var/task/c7n/handler.py", line 165, in dispatch_event p.push(event, context) File "/var/task/c7n/policy.py", line 1288, in push return mode.run(event, lambda_ctx) File "/var/task/c7n/policy.py", line 487, in run resources = self.resolve_resources(event) File "/var/task/c7n/policy.py", line 691, in resolve_resources return super().resolve_resources(event) File "/var/task/c7n/policy.py", line 469, in resolve_resources resources = self.policy.resource_manager.get_resources(resource_ids) File "/var/task/c7n/query.py", line 576, in get_resources resources = self.source.get_resources(ids) File "/var/task/c7n/query.py", line 227, in get_resources return self.query.get(self.manager, ids) File "/var/task/c7n/query.py", line 100, in get resources = self.filter(resource_manager, **params) File "/var/task/c7n/query.py", line 79, in filter return self._invoke_client_enum( File "/var/task/c7n/query.py", line 60, in _invoke_client_enum data = op(**params) File "/var/runtime/botocore/client.py", line 391, in _api_call return self._make_api_call(operation_name, kwargs) File "/var/runtime/botocore/client.py", line 691, in _make_api_call request_dict = self._convert_to_request_dict( File "/var/runtime/botocore/client.py", line 739, in _convert_to_request_dict request_dict = self._serializer.serialize_to_request( File "/var/runtime/botocore/validate.py", line 360, in serialize_to_request raise ParamValidationError(report=report.generate_report()) botocore.exceptions.ParamValidationError: Parameter validation failed: Missing required parameter in input: "Scope"
-- | --
```
### Extra information or context
The pull mode version of this policy works fine.
| [
{
"content": "# Copyright The Cloud Custodian Authors.\n# SPDX-License-Identifier: Apache-2.0\nfrom c7n.manager import resources\nfrom c7n.query import ConfigSource, QueryResourceManager, TypeInfo, DescribeSource\nfrom c7n.tags import universal_augment\nfrom c7n.filters import ValueFilter\nfrom c7n.utils import... | [
{
"content": "# Copyright The Cloud Custodian Authors.\n# SPDX-License-Identifier: Apache-2.0\nfrom c7n.manager import resources\nfrom c7n.query import ConfigSource, QueryResourceManager, TypeInfo, DescribeSource\nfrom c7n.tags import universal_augment\nfrom c7n.filters import ValueFilter\nfrom c7n.utils import... | diff --git a/c7n/resources/waf.py b/c7n/resources/waf.py
index 6970c900e55..4e16cf0c59e 100644
--- a/c7n/resources/waf.py
+++ b/c7n/resources/waf.py
@@ -27,6 +27,10 @@ def get_query_params(self, query):
q = {'Scope': 'REGIONAL'}
return q
+ def get_resources(self, ids):
+ resources = self.query.filter(self.manager, **self.get_query_params(None))
+ return [r for r in resources if r[self.manager.resource_type.id] in ids]
+
@resources.register('waf')
class WAF(QueryResourceManager):
diff --git a/tests/data/placebo/test_wafv2_resolve_resources/tagging.GetResources_1.json b/tests/data/placebo/test_wafv2_resolve_resources/tagging.GetResources_1.json
new file mode 100644
index 00000000000..8b704d1852a
--- /dev/null
+++ b/tests/data/placebo/test_wafv2_resolve_resources/tagging.GetResources_1.json
@@ -0,0 +1,8 @@
+{
+ "status_code": 200,
+ "data": {
+ "PaginationToken": "",
+ "ResourceTagMappingList": [],
+ "ResponseMetadata": {}
+ }
+}
\ No newline at end of file
diff --git a/tests/data/placebo/test_wafv2_resolve_resources/wafv2.ListWebACLs_1.json b/tests/data/placebo/test_wafv2_resolve_resources/wafv2.ListWebACLs_1.json
new file mode 100644
index 00000000000..5bff1bc5858
--- /dev/null
+++ b/tests/data/placebo/test_wafv2_resolve_resources/wafv2.ListWebACLs_1.json
@@ -0,0 +1,23 @@
+{
+ "status_code": 200,
+ "data": {
+ "NextMarker": "dont-match-me",
+ "WebACLs": [
+ {
+ "Name": "match-me",
+ "Id": "624e04d2-8b45-45ee-b4ad-e853dac6d070",
+ "Description": "",
+ "LockToken": "ad4ae4ab-aba8-4499-88e9-059d4f9a4c60",
+ "ARN": "arn:aws:wafv2:us-east-2:644160558196:regional/webacl/match-me/624e04d2-8b45-45ee-b4ad-e853dac6d070"
+ },
+ {
+ "Name": "dont-match-me",
+ "Id": "889cae83-d5e5-41e9-bb6f-00ea0f726637",
+ "Description": "",
+ "LockToken": "a33055a6-2e0a-4e5f-aa8c-abe3a5ce26d8",
+ "ARN": "arn:aws:wafv2:us-east-2:644160558196:regional/webacl/dont-match-me/889cae83-d5e5-41e9-bb6f-00ea0f726637"
+ }
+ ],
+ "ResponseMetadata": {}
+ }
+}
diff --git a/tests/test_waf.py b/tests/test_waf.py
index a9fa1a7b3b6..c7442657a79 100644
--- a/tests/test_waf.py
+++ b/tests/test_waf.py
@@ -17,6 +17,19 @@ def test_waf_query(self):
)
self.assertEqual(resources[0]["DefaultAction"], {"Type": "BLOCK"})
+ def test_wafv2_resolve_resources(self):
+ session_factory = self.replay_flight_data(
+ "test_wafv2_resolve_resources",
+ region="us-east-2"
+ )
+ p = self.load_policy(
+ {"name": "wafv2test", "resource": "aws.wafv2"},
+ session_factory=session_factory,
+ config={"region": "us-east-2"}
+ )
+ resources = p.resource_manager.get_resources(["624e04d2-8b45-45ee-b4ad-e853dac6d070"])
+ assert len(resources) == 1
+
def test_wafv2_logging_configuration(self):
session_factory = self.replay_flight_data(
'test_wafv2_logging_configuration')
|
cloud-custodian__cloud-custodian-6375 | The s3 action "remove-statements" errors out when it encounters a bucket policy statement without a sid
**Describe the bug**
s3.remove-statements fails when a sid-less bucket policy statement is encountered
You can see the key error in the traceback. Bucket policy statements do not require Sids and S3 omits the key from describeBucketPolicy response when it does not exist.
**To Reproduce**
Attempt to use remove-statements to remove a statement from a bucket with a sid-less statement (one example of which is the "aws-sam-cli-managed-default-samclisourcebucket-..." buckets created by AWS SAM CLI.)
**Expected behavior**
I expected the statement which does not contain a SID to be iterated over as non-matching.
**Background (please complete the following information):**
- OS: AWS Lambda
- Python Version: Python 3.8
- Custodian Version: 0.9.8
- Tool Version: n/a
- Cloud Provider: AWS
- Policy: [please exclude any account/sensitive information]
```json
{
"statement_ids": [
"denyAccessToBucket"
],
"type": "remove-statements"
}
```
- Traceback: [if applicable, please exclude sensitive/account information]
[ERROR] KeyError: 'Sid'
Traceback (most recent call last):
File "/var/task/custodian_policy.py", line 4, in run
return handler.dispatch_event(event, context)
File "/var/task/c7n/handler.py", line 165, in dispatch_event
p.push(event, context)
File "/var/task/c7n/policy.py", line 1140, in push
return mode.run(event, lambda_ctx)
File "/var/task/c7n/policy.py", line 853, in run
resources = super(ConfigRuleMode, self).run(event, lambda_context)
File "/var/task/c7n/policy.py", line 453, in run
return self.run_resource_set(event, resources)
File "/var/task/c7n/policy.py", line 483, in run_resource_set
results = action.process(resources)
File "/var/task/c7n/resources/s3.py", line 1272, in process
results += filter(None, [f.result()])
File "/var/lang/lib/python3.8/concurrent/futures/_base.py", line 432, in result
return self.__get_result()
File "/var/lang/lib/python3.8/concurrent/futures/_base.py", line 388, in __get_result
raise self._exception
File "/var/lang/lib/python3.8/concurrent/futures/thread.py", line 57, in run
result = self.fn(*self.args, **self.kwargs)
File "/var/task/c7n/resources/s3.py", line 1282, in process_bucket
statements, found = self.process_policy(
File "/var/task/c7n/actions/policy.py", line 21, in process_policy
return remove_statements(
File "/var/task/c7n/actions/policy.py", line 37, in remove_statements
elif s['Sid'] in match_ids:
- `custodian version --debug` output: n/a
**Additional context**
Add any other context about the problem here.
| [
{
"content": "# Copyright The Cloud Custodian Authors.\n# SPDX-License-Identifier: Apache-2.0\n\nfrom .core import BaseAction\nfrom c7n import utils\n\n\nclass RemovePolicyBase(BaseAction):\n\n schema = utils.type_schema(\n 'remove-statements',\n required=['statement_ids'],\n statement_i... | [
{
"content": "# Copyright The Cloud Custodian Authors.\n# SPDX-License-Identifier: Apache-2.0\n\nfrom .core import BaseAction\nfrom c7n import utils\n\n\nclass RemovePolicyBase(BaseAction):\n\n schema = utils.type_schema(\n 'remove-statements',\n required=['statement_ids'],\n statement_i... | diff --git a/c7n/actions/policy.py b/c7n/actions/policy.py
index c7e2a9e0295..833e1309efe 100644
--- a/c7n/actions/policy.py
+++ b/c7n/actions/policy.py
@@ -31,7 +31,7 @@ def remove_statements(match_ids, statements, matched=()):
elif match_ids == 'matched':
if s in matched:
s_found = True
- elif s['Sid'] in match_ids:
+ elif 'Sid' in s and s['Sid'] in match_ids:
s_found = True
if s_found:
found.append(s)
|
ManimCommunity__manim-732 | Running manim without any flags or render commands throws an error [BUG-General]
It seems as if currently running just `manim` without any flag or scene to render it throws the following error:
```Traceback (most recent call last):
File "c:\users\administrator\appdata\local\programs\python\python38\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "c:\users\administrator\appdata\local\programs\python\python38\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\Administrator\AppData\Local\Programs\Python\Python38\Scripts\manim.exe\__main__.py", line 7, in <module>
File "c:\users\administrator\appdata\local\programs\python\python38\lib\site-packages\manim\__main__.py", line 47, in main
args = parse_args(sys.argv)
File "c:\users\administrator\appdata\local\programs\python\python38\lib\site-packages\manim\_config\main_utils.py", line 110, in parse_args
subcmd = _find_subcommand(args)
File "c:\users\administrator\appdata\local\programs\python\python38\lib\site-packages\manim\_config\main_utils.py", line 39, in _find_subcommand
subcmd = args[1]
IndexError: list index out of range
```
@behackl has mentioned seeing the same bahaviour.
This isn't a major issue since otherwise everything seems to run fine (can still use `manim -h` or render scenes), but somehow who might know what's going on should have a look into it.
| [
{
"content": "\"\"\"Utilities called from ``__main__.py`` to interact with the config.\"\"\"\n\nimport os\nimport sys\nimport argparse\nimport logging\n\nimport colour\n\nfrom manim import constants, logger, config\nfrom .utils import make_config_parser\nfrom .logger_utils import JSONFormatter\nfrom ..utils.tex... | [
{
"content": "\"\"\"Utilities called from ``__main__.py`` to interact with the config.\"\"\"\n\nimport os\nimport sys\nimport argparse\nimport logging\n\nimport colour\n\nfrom manim import constants, logger, config\nfrom .utils import make_config_parser\nfrom .logger_utils import JSONFormatter\nfrom ..utils.tex... | diff --git a/manim/_config/main_utils.py b/manim/_config/main_utils.py
index 1e3075c8f7..c833b37355 100644
--- a/manim/_config/main_utils.py
+++ b/manim/_config/main_utils.py
@@ -107,6 +107,9 @@ def parse_args(args):
if args[0] == "python" and args[1] == "-m":
args = args[2:]
+ if len(args) == 1:
+ return _parse_args_no_subcmd(args)
+
subcmd = _find_subcommand(args)
if subcmd == "cfg":
return _parse_args_cfg_subcmd(args)
|
secdev__scapy-3167 | Outdated Automotive Documentation
Reminder for myself.
Outdated:
https://github.com/secdev/scapy/blob/1aa0d8a849f7b102d18a3f65986e272aec5f518a/doc/scapy/layers/automotive.rst#L75-L85
SOME/IP:
https://github.com/secdev/scapy/blob/1aa0d8a849f7b102d18a3f65986e272aec5f518a/doc/scapy/layers/automotive.rst#L1011-L1030
Mentioned by @WebLabInt via gitter:
```Hi, I m having a problem creating a basic SOME IP service discovery following the example provided https://scapy.readthedocs.io/en/latest/layers/automotive.html?highlight=some%20ip#creating-a-some-ip-sd-message. The SOME IP package is working perfectly, however, the SD packet is not formed correctly thus not recognized as a SD packet by Wireshark and the SOME IP version is not correct. I did a capture with Wireshark reporting those issues http://fuiing.com/share/SD%20prob.png . I will be great if you can support me on this issue, thank you for making Scapy open source, it's really a great tool, have a great day ```
| [
{
"content": "# -*- coding: utf-8 -*-\n#\n# Scapy documentation build configuration file, created by\n# sphinx-quickstart on Wed Mar 07 19:02:35 2018.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration values are present in this... | [
{
"content": "# -*- coding: utf-8 -*-\n#\n# Scapy documentation build configuration file, created by\n# sphinx-quickstart on Wed Mar 07 19:02:35 2018.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration values are present in this... | diff --git a/doc/scapy/backmatter.rst b/doc/scapy/backmatter.rst
index f316bb2a879..326083045e4 100644
--- a/doc/scapy/backmatter.rst
+++ b/doc/scapy/backmatter.rst
@@ -8,3 +8,4 @@ Credits
- Fred Raynal wrote the chapter on building and dissecting packets.
- Peter Kacherginsky contributed several tutorial sections, one-liners and recipes.
- Dirk Loss integrated and restructured the existing docs to make this book.
+- Nils Weiss contributed automotive specific layers and utilities.
diff --git a/doc/scapy/conf.py b/doc/scapy/conf.py
index 981e8f0072f..33c4615ba8d 100644
--- a/doc/scapy/conf.py
+++ b/doc/scapy/conf.py
@@ -97,6 +97,9 @@
# If true, `todo` and `todoList` produce output, else they produce nothing.
todo_include_todos = False
+# Enable codeauthor and sectionauthor directives
+show_authors = True
+
# -- Options for HTML output ----------------------------------------------
diff --git a/doc/scapy/graphics/automotive/CAN-full-frame.jpg b/doc/scapy/graphics/automotive/CAN-full-frame.jpg
new file mode 100644
index 00000000000..d726b3b5c7c
Binary files /dev/null and b/doc/scapy/graphics/automotive/CAN-full-frame.jpg differ
diff --git a/doc/scapy/graphics/automotive/DC-ZGW-CAN-Bus-.png b/doc/scapy/graphics/automotive/DC-ZGW-CAN-Bus-.png
new file mode 100644
index 00000000000..ea778feda1a
Binary files /dev/null and b/doc/scapy/graphics/automotive/DC-ZGW-CAN-Bus-.png differ
diff --git a/doc/scapy/graphics/automotive/Simple-CAN-Bus-.png b/doc/scapy/graphics/automotive/Simple-CAN-Bus-.png
new file mode 100644
index 00000000000..d767795911a
Binary files /dev/null and b/doc/scapy/graphics/automotive/Simple-CAN-Bus-.png differ
diff --git a/doc/scapy/graphics/automotive/XCP_ReferenceBook.png b/doc/scapy/graphics/automotive/XCP_ReferenceBook.png
new file mode 100644
index 00000000000..e970660c73d
Binary files /dev/null and b/doc/scapy/graphics/automotive/XCP_ReferenceBook.png differ
diff --git a/doc/scapy/graphics/automotive/ZGW-CAN-Bus-.png b/doc/scapy/graphics/automotive/ZGW-CAN-Bus-.png
new file mode 100644
index 00000000000..80a3e9c51c9
Binary files /dev/null and b/doc/scapy/graphics/automotive/ZGW-CAN-Bus-.png differ
diff --git a/doc/scapy/graphics/automotive/can-bus-states.png b/doc/scapy/graphics/automotive/can-bus-states.png
new file mode 100644
index 00000000000..389a00766ff
Binary files /dev/null and b/doc/scapy/graphics/automotive/can-bus-states.png differ
diff --git a/doc/scapy/graphics/automotive/can-frame-socket-can.png b/doc/scapy/graphics/automotive/can-frame-socket-can.png
new file mode 100644
index 00000000000..efd96e03147
Binary files /dev/null and b/doc/scapy/graphics/automotive/can-frame-socket-can.png differ
diff --git a/doc/scapy/graphics/automotive/diag-stack.png b/doc/scapy/graphics/automotive/diag-stack.png
new file mode 100644
index 00000000000..76a7b11635e
Binary files /dev/null and b/doc/scapy/graphics/automotive/diag-stack.png differ
diff --git a/doc/scapy/graphics/automotive/isotp-flow.png b/doc/scapy/graphics/automotive/isotp-flow.png
new file mode 100644
index 00000000000..08e7a7b944f
Binary files /dev/null and b/doc/scapy/graphics/automotive/isotp-flow.png differ
diff --git a/doc/scapy/graphics/automotive/isotp-frames.png b/doc/scapy/graphics/automotive/isotp-frames.png
new file mode 100644
index 00000000000..a724f95e5fd
Binary files /dev/null and b/doc/scapy/graphics/automotive/isotp-frames.png differ
diff --git a/doc/scapy/layers/automotive.rst b/doc/scapy/layers/automotive.rst
index a4a1a08a9a9..83fc509df26 100644
--- a/doc/scapy/layers/automotive.rst
+++ b/doc/scapy/layers/automotive.rst
@@ -1,150 +1,284 @@
-**********
-Automotive
-**********
+.. note:: This document is under a `Creative Commons Attribution - Non-Commercial - Share Alike 2.5 <http://creativecommons.org/licenses/by-nc-sa/2.5/>`_ license.
+#################################
+Automotive-specific Documentation
+#################################
+
+.. sectionauthor:: Nils Weiss <nils@we155.de>
+
+********
Overview
-========
+********
.. note::
- All automotive related features work best on Linux systems. CANSockets and ISOTPSockets in Scapy are based on Linux kernel modules.
- The python-can project is used to support CAN and CANSockets on other systems, besides Linux.
- This guide explains the hardware setup on a BeagleBone Black. The BeagleBone Black was chosen because of its two CAN interfaces on the main processor.
- The presence of two CAN interfaces in one device gives the possibility of CAN MITM attacks and session hijacking.
- The Cannelloni framework turns a single board computer into a CAN-to-UDP interface, which gives you the freedom to run Scapy
- on a more powerful machine.
+ All automotive-related features work best on Linux systems. CANSockets and ISOTPSockets are based on Linux kernel modules. The python-can project is used to support CAN and CANSockets on a wider range of operating systems and CAN hardware interfaces.
Protocols
----------
+=========
-The following table should give a brief overview about all automotive capabilities
+The following table should give a brief overview of all the automotive-related capabilities
of Scapy. Most application layer protocols have many specialized ``Packet`` classes.
-These special purpose classes are not part of this overview. Use the ``explore()``
+These special-purpose ``Packets`` are not part of this overview. Use the ``explore()``
function to get all information about one specific protocol.
-+---------------------+----------------------+--------------------------------------------------------+
-| OSI Layer | Protocol | Scapy Implementations |
-+=====================+======================+========================================================+
-| Application Layer | UDS (ISO 14229) | UDS, UDS_*, UDS_TesterPresentSender |
-| +----------------------+--------------------------------------------------------+
-| | GMLAN | GMLAN, GMLAN_*, GMLAN_TesterPresentSender |
-| +----------------------+--------------------------------------------------------+
-| | SOME/IP | SOMEIP, SD |
-| +----------------------+--------------------------------------------------------+
-| | BMW HSFZ | HSFZ, HSFZSocket |
-| +----------------------+--------------------------------------------------------+
-| | OBD | OBD, OBD_S0X |
-| +----------------------+--------------------------------------------------------+
-| | CCP | CCP, DTO, CRO |
-| +----------------------+--------------------------------------------------------+
-| | XCP | XCPOnCAN, XCPOnUDP, XCPOnTCP, CTORequest, CTOResponse, |
-| | | DTO |
-+---------------------+----------------------+--------------------------------------------------------+
-| Transportation Layer| ISO-TP (ISO 15765-2) | ISOTPSocket, ISOTPNativeSocket, ISOTPSoftSocket |
-| | | |
-| | | ISOTPSniffer, ISOTPMessageBuilder, ISOTPSession |
-| | | |
-| | | ISOTPHeader, ISOTPHeaderEA, ISOTPScan |
-| | | |
-| | | ISOTP, ISOTP_SF, ISOTP_FF, ISOTP_CF, ISOTP_FC |
-+---------------------+----------------------+--------------------------------------------------------+
-| Data Link Layer | CAN (ISO 11898) | CAN, CANSocket, rdcandump, CandumpReader |
-+---------------------+----------------------+--------------------------------------------------------+
-
-
-CAN Layer
-=========
++----------------------+----------------------+--------------------------------------------------------+
+| OSI Layer | Protocol | Scapy Implementations |
++======================+======================+========================================================+
+| Application Layer | UDS (ISO 14229) | UDS, UDS_*, UDS_TesterPresentSender |
+| +----------------------+--------------------------------------------------------+
+| | GMLAN | GMLAN, GMLAN_*, GMLAN_[Utilities] |
+| +----------------------+--------------------------------------------------------+
+| | SOME/IP | SOMEIP, SD |
+| +----------------------+--------------------------------------------------------+
+| | BMW HSFZ | HSFZ, HSFZSocket, ISOTP_HSFZSocket |
+| +----------------------+--------------------------------------------------------+
+| | OBD | OBD, OBD_S0[0-9A] |
+| +----------------------+--------------------------------------------------------+
+| | CCP | CCP, DTO, CRO |
+| +----------------------+--------------------------------------------------------+
+| | XCP | XCPOnCAN, XCPOnUDP, XCPOnTCP, CTORequest, CTOResponse, |
+| | | DTO |
++----------------------+----------------------+--------------------------------------------------------+
+| Transportation Layer | ISO-TP (ISO 15765-2) | ISOTPSocket, ISOTPNativeSocket, ISOTPSoftSocket |
+| | | |
+| | | ISOTPSniffer, ISOTPMessageBuilder, ISOTPSession |
+| | | |
+| | | ISOTPHeader, ISOTPHeaderEA, ISOTPScan |
+| | | |
+| | | ISOTP, ISOTP_SF, ISOTP_FF, ISOTP_CF, ISOTP_FC |
++----------------------+----------------------+--------------------------------------------------------+
+| Data Link Layer | CAN (ISO 11898) | CAN, CANSocket, rdcandump, CandumpReader |
++----------------------+----------------------+--------------------------------------------------------+
+
+
+********************
+Technical Background
+********************
+
+Parts this section were published in a study report [10]_.
+
+Physical Protocols
+==================
+
+More than 20 different communication protocols exist for the vehicle’s internal wired communication. Most vehicles make use of five to ten different protocols for their internal communication. The decision which communication protocol is used from an Original Equipment Manufacturer (OEM) is usually made by the trade-off between the costs for communication technology and the final car price. The four major communication technologies for inter-ECU communication are Controller Area Network (CAN), FlexRay, Local Interconnect Network (LIN), and Automotive Ethernet. For security considerations, these are the most relevant protocols for wired communication in vehicles.
+
+LIN
+---
+LIN is a single wire communication protocol for low data rates. Actuators and sensors of a vehicle exchange information with an ECU, acting as a LIN master. Software updates over LIN are possible, but the LIN slaves usually do not need software updates because of their limited functionality.
+
+CAN
+---
+CAN is by far the most used communication technology for inter-ECU communication in vehicles. In older or cheaper vehicles, CAN is still the primary protocol for a vehicle’s backbone communication. Safety-critical communication during a vehicle’s operation, diagnostic information, and software updates are transferred between ECUs over CAN. The lack of security features in the protocol itself, combined with the general use, makes CAN the primary protocol for security investigations.
+
+FlexRay
+-------
+The FlexRay consortium designed FlexRay as a successor of CAN. Modern vehicles have higher demands on communication bandwidth. By design, FlexRay is a fast and reliable communication protocol for inter-ECU communication. FlexRay components are more expensive than CAN components, leading to a more selective use by OEMs.
+
+Automotive Ethernet
+-------------------
+Recent upper-class vehicles implement Automotive Ethernet, the new backbone technology for internal vehicle communication. The rapidly grown bandwidth demands already replace FlexRay. The primary reasons for these demands are driver-assistant and autonomous-driving features. Only the physical layer (layer 1) of the Open Systems Interconnection (OSI) model distinguishes Ethernet (IEEE 802.3) from Automotive Ethernet (BroadR-Reach). This design decision leads to multiple advantages. For example, communication stacks of operating systems can be used without modification and routing, filtering, and firewall systems. Automotive Ethernet components are already cheaper than FlexRay components, which will lead to vehicle topologies, where CAN and Automotive Ethernet are the most used communication protocols.
-How-To
+Topologies
+==========
+
+Line-Bus
--------
-Send and receive a message over Linux SocketCAN::
+.. _fig-line-bus:
- load_layer("can")
- load_contrib('cansocket')
+.. figure:: ../graphics/automotive/Simple-CAN-Bus-.png
- socket = CANSocket(channel='can0')
- packet = CAN(identifier=0x123, data=b'01020304')
+ Line-Bus network topology
- socket.send(packet)
- rx_packet = socket.recv()
+The first vehicles with CAN bus used a single network with a line-bus topology. Some lower-priced vehicles still use one or two shared CAN bus networks for their internal communication nowadays. The downside of this topology is its vulnerability and the lack of network separation. All ECUs of a vehicle are connected on a shared bus. Since CAN does not support security features from its protocol definition, any participant on this bus can communicate directly with all other participants, which allows an attacker to affect all ECUs, even safety-critical ones, by compromising one single ECU. The overall security level of this network is given from the security level of the weakest participant.
- socket.sr1(packet, timeout=1)
+Central Gateway
+---------------
-Send a message over a Vector CAN-Interface::
+.. _fig-cgw:
- import can
- load_layer("can")
- conf.contribs['CANSocket'] = {'use-python-can' : True}
- load_contrib('cansocket')
- from can.interfaces.vector import VectorBus
+.. figure:: ../graphics/automotive/ZGW-CAN-Bus-.png
- socket = CANSocket(channel=VectorBus(0, bitrate=1000000))
- packet = CAN(identifier=0x123, data=b'01020304')
+ Network topology with central GW ECU
- socket.send(packet)
- rx_packet = socket.recv()
+The central Gateway (GW) topology can be found in higher-priced older cars and medium-priced to lower-priced recent cars. A centralized GW ECU separates domain-specific sub-networks. This allows an OEM to encapsulate all ECUs with remote attack surfaces in one sub-network. ECUs with safety-critical functionalities are located in an individual CAN network. Next to CAN, FlexRay might also be used as a communication protocol inside a separate network domain. The security of a safety-critical network in this topology depends mainly on the central GW ECU’s security. This architecture increases the overall security level of a vehicle through domain separation. After an attacker successfully exploited an ECU through an arbitrary attack surface, a second exploitable vulnerability or a logical bug is necessary to compromise a different domain, a safety-critical network, inside a vehicle. This second exploit or logical bug is necessary to overcome the network separation of the central GW ECU.
- socket.sr1(packet)
+Central Gateway and Domain Controller
+-------------------------------------
+.. _fig-dc:
+.. figure:: ../graphics/automotive/DC-ZGW-CAN-Bus-.png
-Tutorials
----------
+ Network topology with Automotive-Ethernet backbone and DC
-Linux SocketCAN
-^^^^^^^^^^^^^^^
+A new topology with central GW and Domain Controllers (DCs) can be found in the latest higher-priced vehicles. The increasing demand for bandwidth in modern vehicles with autonomous driving and driver assistant features led to this topology. An Automotive Ethernet network is used as a communication backbone for the entire vehicle. Individual domains, connected through a DC with the central GW, form the vehicle’s backbone. The individual DCs can control and regulate the data communication between a domain and the vehicle’s backbone. This topology achieves a very-high security level through a strong network separation with individual DCs, acting as gateway and firewall, to the vehicle’s backbone network. OEMs have the advantage of dynamic information routing next to this security improvement, an enabler for Feature on Demand (FoD) services.
-This subsection summarizes some basics about Linux SocketCAN. An excellent overview
-from Oliver Hartkopp can be found here: https://wiki.automotivelinux.org/_media/agl-distro/agl2017-socketcan-print.pdf
+Automotive Communication Protocols
+==================================
-Virtual CAN Setup
-^^^^^^^^^^^^^^^^^
+This section provides an overview of relevant communication protocols for security evaluations in automotive networks. In contrast to section "Physical Protocols", this section focuses on properties for data communication.
-Linux SocketCAN supports virtual CAN interfaces. These interfaces are an easy way
-to do some first steps on a CAN-Bus without the requirement of special hardware.
-Besides that, virtual CAN interfaces are heavily used in Scapy unit test for automotive
-related contributions.
+CAN
+---
-Virtual CAN sockets require a special Linux kernel module. The following shell command loads the required module::
+The CAN communication technology was invented in 1983 as a message-based robust vehicle bus communication system. The Robert Bosch GmbH designed multiple communication features into the CAN standard to achieve a robust and computation efficient protocol for controller area networks. Remarkable for the communication behavior of CAN is the internal state machine for transmission errors. This state machine implements a fail silent behavior to protect a safety-critical network from babbling idiot nodes. If a specific limit of reception errors (REC) or transmission errors (TEC) occurred, the CAN driver changes its state from error-active to error-passive and finally to bus-off.
- sudo modprobe vcan
+.. _fig-can-bus-states:
-In order to use a virtual CAN interface some additional commands for setup are required.
-This snippet chooses the name ``vcan0`` for the virtual CAN interface. Any name can be chosen here::
+.. figure:: ../graphics/automotive/can-bus-states.png
- sudo ip link add name vcan0 type vcan
- sudo ip link set dev vcan0 up
+ CAN bus states on transmission errors. Receive Error Counter (REC), Transmit Error Counter (TEC)
-The same commands can be executed from Scapy like this::
+In recent years, this protocol specification was abused for Denial of Service (DoS) attacks and information gathering attacks on the CAN network of a vehicle. Cho et al. demonstrated a DoS attack against CAN networks by abusing the bus-off state of ECUs [1]_. Injections of communication errors in CAN frames of one specific node caused a high transmission error count in the node under attack, forcing the attacked node to enter the bus-off state. In 2019 Kulandaivel et al. combined this attack with statistical analysis to achieve a fast and inexpensive network mapping in vehicular networks [2]_. They combined statistical analysis of the CAN network traffic before and after the bus-off attack was applied to a node. All missing CAN frames in the network traffic after an ECU was attacked could now be mapped to the ECU under attack, helping researchers identify the origin ECU of a CAN frame. Ken Tindell published a comprehensive summary of low level attacks on CANs in 2019 [3]_.
- from scapy.layers.can import *
- import os
+.. _fig-can-full-frame:
- bashCommand = "/bin/bash -c 'sudo modprobe vcan; sudo ip link add name vcan0 type vcan; sudo ip link set dev vcan0 up'"
- os.system(bashCommand)
+.. figure:: ../graphics/automotive/CAN-full-frame.jpg
-If it's required, a CAN interface can be set into a ``listen-only`` or ``loopback`` mode with ``ip link set`` commands::
+ Complete CAN data frame structure [9]_
- ip link set vcan0 type can help # shows additional information
+The above figure shows a CAN frame and its fields as it is transferred over the network. For information exchange, only the fields arbitration, control, and data are relevant. These are the only fields to which a usual application software has access. All other fields are evaluated on a hardware-layer and, in most cases, are not forwarded to an application. The data field has a variable length and can hold up to eight bytes. The length of the data field is specified by the data length code inside the control field. Important variations of this example are CAN-frames with extended arbitration fields and the Controller Area Network Flexible Data-Rate (CAN FD) protocol. On Linux, every received CAN frame is passed to SocketCAN. SocketCAN allows the CAN handling via network sockets of the operating system. SocketCAN was created by Oliver Hartkopp and added to the Linux Kernel version 2.6.25 [4]_. Figure 2.7 shows the frame structure, how CAN frames are encoded if a user-land application receives data from a CAN socket.
+.. _fig-can-socket-frame:
-Linux can-utils
-^^^^^^^^^^^^^^^
+.. figure:: ../graphics/automotive/can-frame-socket-can.png
-As part of Linux SocketCAN, some very useful commandline tools are provided from Oliver Hartkopp: https://github.com/linux-can/can-utils
+ CAN frame defined by SocketCAN
-The following example shows basic functions of Linux can-utils. These utilities are very handy for
-quick checks, dumping, sending or logging of CAN messages from the command line.
+The comparison of above figures clearly shows the loss of information during the CAN frame processing from a physical layer driver. Almost every CAN driver acts in the same way, whether an application code runs on a microcontroller or a Linux kernel. This also means that a standard application does not have access to the Cyclic Redundancy Check (CRC) field, the acknowledgment bit, or the end-of-frame field.
+
+Through the CAN communication in a vehicle or a separated domain, ECUs exchange sensor-data and control inputs; this data is mainly not secured and can be modified by assailants. Attackers can easily spoof sensor values on a CAN bus to trigger malicious reactions of other ECUs. Miller and Valasek described this spoofing attack during their studies on automotive networks [5]_. To prevent attacks on safety-critical data transferred over CAN, Automotive Open System Architecture (AUTOSAR) released a secure onboard communication specification [6]_.
+
+ISO-TP (ISO 15765-2)
+--------------------
+
+The CAN protocol supports only eight bytes of data. Use-cases like diagnostic operations or ECU programming require much higher payloads than the CAN protocol supports. For these purposes, the automotive industry standardized the Transport Layer (ISO-TP) (ISO 15765-2) protocol [7]_. ISO-TP is a transportation layer protocol on top of CAN. Payloads with up to 4095 bytes can be transferred between ISO-TP endpoints fragmented in CAN frames. The ISO-TP protocol handling requires four special frame types.
+
+.. _fig-isotp-flow:
+
+.. figure:: ../graphics/automotive/isotp-flow.png
+
+ ISO-TP fragmented communication
+
+The different types of ISO-TP frames are shown in the following figure. The payload of a CAN frame gets replaced by one of the four ISO-TP frames. The individual ISO-TP frames have different purposes. A single frame can transfer between 1 and 7 bytes of ISO-TP message data. The len field of a Single Frame or a First Frame indicates the ISO-TP message length. Every message with more than 7 bytes of payload data must be fragmented into a First Frame, followed by multiple Consecutive Frames. This communication is illustrated in the above figure. After the First Frame is sent from a sender, the receiver has to communicate its reception capabilities through a Flow Control Frame to the sender. Only after this Flow Control Frame is received, the sender is allowed to communicate the Consecutive Frames according to the receiver’s capabilities.
+
+.. _fig-isotp-frames:
+
+.. figure:: ../graphics/automotive/isotp-frames.png
+
+ ISO-TP frame types
+
+ISO-TP acts as a transport protocol with the support of directed communication through addressing mechanisms. In vehicles, ISO-TP is mainly used as a transport protocol for diagnostic communication. In rare cases, ISO-TP is also used to exchange larger data between ECUs of a vehicle. Security measures have to be applied to the application layer protocol transported through ISO-TP since ISO-TP has no capabilities to secure its transported data.
+
+DoIP
+----
+
+Diagnostic over IP (DoIP) was first implemented on automotive networks with a centralized gateway topology. A centralized GW functions as a DoIP endpoint that routes diagnostic messages to the desired network, allowing manufacturers to program or diagnose multiple ECUs in parallel. Since the Internet Protocol (IP) communication between a repair-shop tester and the GW is many times faster than the communication between the GW ECU and a target ECU connected over CAN, the remaining bandwidth of the IP communication can be used to start further DoIP connections to other ECUs in different CAN domains. DoIP is specified as part of AUTOSAR and in ISO 13400-2. Similar to ISO-TP, DoIP does not specify special security measures. The responsibility regarding secured communication is delegated to the application layer protocol.
+
+Diagnostic Protocols
+--------------------
+
+Two examples of diagnostic protocols are General Motor Local Area Network (GMLAN) and Unified Diagnostic Service (UDS) (ISO 14229-2). The General Motors Cooperation uses GMLAN. German OEMs mainly use UDS. Both protocols are very similar from a specification point of view, and both protocols use either ISO-TP or DoIP messages for a directed communication with a target ECU. Since different OEMs use UDS, every manufacturer adds its custom additions to the standard. Also, every manufacturer uses individual ISO-TP addressing for the directed communication with an ECU. GMLAN includes more precise definitions about ECU addressing and an ECUs internal behavior compared to UDS.
+
+UDS and GMLAN follow a tree-like message structure, where the first byte identifies the service. Every service is answered by a response. Two types of responses are defined in the standard. Negative responses are indicated through the service 0x7F. Positive responses are identified by the request service identifier incremented with 0x40.
+
+.. _fig-diag-stack:
+
+.. figure:: ../graphics/automotive/diag-stack.png
+
+ Automotive Diagnostic Protocol Stack
+
+A clear separation between the transport and the application layer allows creating application layer tools for both network stacks. The figure above provides an overview of relevant protocols and the corresponding layers. UDS defines a clean separation between application and transport layer. On CAN based networks, ISO-TP is used for this purpose. The CAN protocol can be treated as the network access protocol. This allows to replace ISO-TP and CAN with DoIP or HSFZ and Ethernet. The GMLAN protocol combines transport and application layer specifications very similar to ISO-TP and UDS. Because of that similarity, identical application layer-specific scan techniques can be applied. To overcome the bandwidth limitations of CAN, the latest vehicle architectures use an Ethernet-based diagnostic protocol (DoIP, HSFZ) to communicate with a central gateway ECU. The central gateway ECU routes application layer packets from an Ethernet-based network to a CAN based vehicle internal network. In general, the diagnostic functions of all ECUs in a vehicle can be accessed from the OBD connector over UDSonCAN or UDSonIP.
+
+SOME/IP
+-------
+
+Scalable service-Oriented MiddlewarE over IP (SOME/IP) defines a new philosophy of data communication in automotive networks. SOME/IP is used to exchange data between network domain controllers in the latest vehicle networks. SOME/IP supports subscription and notification mechanisms, allowing domain controllers to dynamically subscribe to data provided by another domain controller dependent on the vehicle’s state. SOME/IP transports data between domain controllers and the gateway that a vehicle needs during its regular operation. The use-cases of SOME/IP are similar to the use-cases of CAN communication. The main purpose is the information exchange of sensor and actuator data between ECUs. This usage emphasizes SOME/IP communication as a rewarding target for cyber-attacks.
+
+CCP/XCP
+-------
+
+Universal Measurement and Calibration Protocol (XCP), the CAN Calibration Protocol (CCP) successor, is a calibration protocol for automotive systems, standardized by ASAM e.V. in 2003. The primary usage of XCP is during the testing and calibration phase of ECU or vehicle development. CCP is designed for use on CAN. No message in CCP exceeds the 8-byte limitation of CAN. To overcome this restriction, XCP was designed to aim for compatibility with a wide range of transport protocols. XCP can be used on top of CAN, CAN FD, Serial Peripheral Interface (SPI), Ethernet, Universal Serial Bus (USB), and FlexRay. The features of CCP and XCP are very similar; however, XCP has a larger functional scope and optimizations for data efficiency.
+
+Both protocols have a session-based communication procedure and support authentication through seed and key mechanisms between a master and multiple slave nodes. A master node is typically an engineering Personal Computer (PC). In vehicles, slave nodes are ECUs for configuration. XCP also supports simulation. A vehicle engineer can debug a MATLAB Simulink model through XCP. In this case, the simulated model acts as the XCP slave node. CCP and XCP can read and write to the memory of an ECU. Another main feature is data acquisition. Both protocols support a procedure that allows an engineer to configure a so-called data acquisition list with memory addresses of interest. All memory specified in such a list will be read periodically and be broadcast in a CCP or XCP Data Acquisition (DAQ) packet on the chosen communication channel. The following figure gives an overview of all supported communication and packet types in XCP. In the Command Transfer Object (CTO) area, all communication follows a request and response procedure always initiated by the XCP master. A Command Packet (CMD) can receive a Command Response Packet (RES), an Error (ERR) packet, an Event Packet (EV), or a Service Request Packet (SERV) as a response. After the configuration of a slave through CTO CMDs, a slave can listen for Stimulation (STIM) packets and periodically send configured DAQ packets. The resources section in the following figure indicates the possible attack surfaces of this protocol (Programming (PGM), Calibration (CAL), DAQ, STIM) which an attacker could abuse. It is crucial for a vehicle’s security and safety that such protocols, which have their use only during calibration and development of a vehicle, are disabled or removed before a vehicle is shipped to a customer.
+
+.. _fig-xcp-reference:
+
+.. figure:: ../graphics/automotive/XCP_ReferenceBook.png
+
+ XCP communication model between XCP Master and XCP Slave. This model shows the communication direction for CTO/Data Transfer Object (DTO) packages [8]_.
+
+**References**
+
+.. [1] Kyong-Tak Cho and Kang G. Shin. Error handling of in-vehicle networks makes them vulnerable. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, CCS ’16, page 1044–1055, New York, NY, USA, 2016. Association for Computing Machinery.
+
+.. [2] Sekar Kulandaivel, Tushar Goyal, Arnav Kumar Agrawal, and Vyas Sekar. Canvas: Fast and inexpensive automotive network mapping. In 28th USENIX Security Symposium (USENIX Security 19), pages 389–405, Santa Clara, CA, August 2019. USENIX Association.
+
+.. [3] Ken Tindell. CAN Bus Security - Attacks on CAN bus and their mitigations, 2019. https://canislabs.com/wp-content/uploads/2020/12/2020-02-14-White-Paper-CAN-Security.pdf
+
+.. [4] Oliver Hartkopp. Readme file for the Controller Area Network Protocol Family (aka SocketCAN), 2020 (accessed January 29, 2020). https://www.kernel.org/doc/Documentation/networking/can.txt
+
+.. [5] Dr. Charlie Miller and Chris Valasek. Adventures in Automotive Networks and Control Units. DEF CON 21 Hacking Conference. Las Vegas, NV: DEF CON, August 2013. http://illmatics.com/car_hacking.pdf (accessed 2020-05-27)
+
+.. [6] AUTOSAR. Specification of Secure Onboard Communication, 2020 (accessed January 31, 2020). https://www.autosar.org/fileadmin/user_upload/standards/classic/4-3/AUTOSAR_SWS_SecureOnboardCommunication.pdf
+
+.. [7] ISO Central Secretary. Road vehicles – Diagnostic communication over Controller Area Network (DoCAN) – Part 2: Transport protocol and network layer services. Standard ISO 15765-2:2016, International Organization for Standardization, Geneva, CH, 2016.
+
+.. [8] Vector Informatik GmbH. XCP – The Standard Protocol for ECU Development. Vector Informatik GmbH, 2020 (accessed January 30, 2020). https://assets.vector.com/cms/content/application-areas/ecu-calibration/xcp/XCP_ReferenceBook_V3.0_EN.pdf
+
+.. [9] Pico Technology Ltd. Complete CAN data frame structure, 2020 (accessed February 14, 2020). https://www.picotech.com/images/uploads/library/topics/_med/CAN-full-frame.jpg
+
+.. [10] Nils Weiss. Security Testing in Safety-Critical Networks. PhD Study Report. http://www.kiv.zcu.cz/site/documents/verejne/vyzkum/publikace/technicke-zpravy/2020/Rigo_Weiss_2020_2.pdf
+
+
+******
+Layers
+******
+
+.. note:: **ATTENTION**: Animations below might be outdated.
+
+CAN
+===
+
+How-To
+------
+
+Send and receive a message over Linux SocketCAN::
+
+ load_layer("can")
+ load_contrib('cansocket')
+
+ socket = CANSocket(channel='can0')
+ packet = CAN(identifier=0x123, data=b'01020304')
+
+ socket.send(packet)
+ rx_packet = socket.recv()
+
+ socket.sr1(packet, timeout=1)
+
+Send and receive a message over a Vector CAN-Interface::
+
+ load_layer("can")
+ conf.contribs['CANSocket'] = {'use-python-can' : True}
+ load_contrib('cansocket')
+
+ socket = CANSocket(bustype='vector', channel=0, bitrate=1000000)
+ packet = CAN(identifier=0x123, data=b'01020304')
+
+ socket.send(packet)
+ rx_packet = socket.recv()
+
+ socket.sr1(packet)
-.. image:: ../graphics/animations/animation-cansend.svg
CAN Frame
-^^^^^^^^^
+---------
Basic information about CAN can be found here: https://en.wikipedia.org/wiki/CAN_bus
-The following examples assume that CAN layer in your Scapy session is loaded. If it isn't,
-the CAN layer can be loaded with this command in your Scapy session::
+The following examples assume that CAN layer in your Scapy session is loaded.
+If it isn't, the CAN layer can be loaded with this command in your Scapy session::
>>> load_layer("can")
@@ -166,8 +300,9 @@ Creation of an extended CAN frame::
.. image:: ../graphics/animations/animation-scapy-canframe.svg
+
CAN Frame in- and export
-^^^^^^^^^^^^^^^^^^^^^^^^
+------------------------
CAN Frames can be written to and read from ``pcap`` files::
@@ -186,9 +321,127 @@ This allows you to use ``sniff`` and other functions from Scapy::
.. image:: ../graphics/animations/animation-scapy-rdcandump.svg
-Scapy CANSocket
+
+DBC File Format and CAN Signals
+-------------------------------
+
+In order to support the DBC file format, ``SignalFields`` and the ``SignalPacket``
+classes were added to Scapy. ``SignalFields`` should only be used inside a ``SignalPacket``.
+Multiplexer fields (MUX) can be created through ``ConditionalFields``. The following
+example demonstrates the usage::
+
+ DBC Example:
+
+ BO_ 4 muxTestFrame: 7 TEST_ECU
+ SG_ myMuxer M : 53|3@1+ (1,0) [0|0] "" CCL_TEST
+ SG_ muxSig4 m0 : 25|7@1- (1,0) [0|0] "" CCL_TEST
+ SG_ muxSig3 m0 : 16|9@1+ (1,0) [0|0] "" CCL_TEST
+ SG_ muxSig2 m0 : 15|8@0- (1,0) [0|0] "" CCL_TEST
+ SG_ muxSig1 m0 : 0|8@1- (1,0) [0|0] "" CCL_TEST
+ SG_ muxSig5 m1 : 22|7@1- (0.01,0) [0|0] "" CCL_TEST
+ SG_ muxSig6 m1 : 32|9@1+ (2,10) [0|0] "mV" CCL_TEST
+ SG_ muxSig7 m1 : 2|8@0- (0.5,0) [0|0] "" CCL_TEST
+ SG_ muxSig8 m1 : 0|6@1- (10,0) [0|0] "" CCL_TEST
+ SG_ muxSig9 : 40|8@1- (100,-5) [0|0] "V" CCL_TEST
+
+ BO_ 3 testFrameFloat: 8 TEST_ECU
+ SG_ floatSignal2 : 32|32@1- (1,0) [0|0] "" CCL_TEST
+ SG_ floatSignal1 : 7|32@0- (1,0) [0|0] "" CCL_TEST
+
+Scapy implementation of this DBC description::
+
+ class muxTestFrame(SignalPacket):
+ fields_desc = [
+ LEUnsignedSignalField("myMuxer", default=0, start=53, size=3),
+ ConditionalField(LESignedSignalField("muxSig4", default=0, start=25, size=7), lambda p: p.myMuxer == 0),
+ ConditionalField(LEUnsignedSignalField("muxSig3", default=0, start=16, size=9), lambda p: p.myMuxer == 0),
+ ConditionalField(BESignedSignalField("muxSig2", default=0, start=15, size=8), lambda p: p.myMuxer == 0),
+ ConditionalField(LESignedSignalField("muxSig1", default=0, start=0, size=8), lambda p: p.myMuxer == 0),
+ ConditionalField(LESignedSignalField("muxSig5", default=0, start=22, size=7, scaling=0.01), lambda p: p.myMuxer == 1),
+ ConditionalField(LEUnsignedSignalField("muxSig6", default=0, start=32, size=9, scaling=2, offset=10, unit="mV"), lambda p: p.myMuxer == 1),
+ ConditionalField(BESignedSignalField("muxSig7", default=0, start=2, size=8, scaling=0.5), lambda p: p.myMuxer == 1),
+ ConditionalField(LESignedSignalField("muxSig8", default=0, start=3, size=3, scaling=10), lambda p: p.myMuxer == 1),
+ LESignedSignalField("muxSig9", default=0, start=41, size=7, scaling=100, offset=-5, unit="V"),
+ ]
+
+ class testFrameFloat(SignalPacket):
+ fields_desc = [
+ LEFloatSignalField("floatSignal2", default=0, start=32),
+ BEFloatSignalField("floatSignal1", default=0, start=7)
+ ]
+
+ bind_layers(SignalHeader, muxTestFrame, identifier=0x123)
+ bind_layers(SignalHeader, testFrameFloat, identifier=0x321)
+
+ dbc_sock = CANSocket("can0", basecls=SignalHeader)
+
+ pkt = SignalHeader()/testFrameFloat(floatSignal2=3.4)
+
+ dbc_sock.send(pkt)
+
+This example uses the class ``SignalHeader`` as header. The payload is specified by individual ``SignalPackets``.
+``bind_layers`` combines the header with the payload dependent on the CAN identifier.
+If you want to directly receive ``SignalPackets`` from your ``CANSocket``, provide the parameter ``basecls`` to
+the ``init`` function of your ``CANSocket``.
+
+Canmatrix supports the creation of Scapy files from DBC or AUTOSAR XML files https://github.com/ebroecker/canmatrix
+
+
+CANSockets
+==========
+
+Linux SocketCAN
+---------------
+
+This subsection summarizes some basics about Linux SocketCAN. An excellent overview
+from Oliver Hartkopp can be found here: https://wiki.automotivelinux.org/_media/agl-distro/agl2017-socketcan-print.pdf
+
+Virtual CAN Setup
+^^^^^^^^^^^^^^^^^
+
+Linux SocketCAN supports virtual CAN interfaces. These interfaces are an easy way
+to do some first steps on a CAN-Bus without the requirement of special hardware.
+Besides that, virtual CAN interfaces are heavily used in Scapy unit tests for
+automotive-related contributions.
+
+Virtual CAN sockets require a special Linux kernel module. The following shell command loads the required module::
+
+ sudo modprobe vcan
+
+In order to use a virtual CAN interface some additional commands for setup are required.
+This snippet chooses the name ``vcan0`` for the virtual CAN interface. Any name can be chosen here::
+
+ sudo ip link add name vcan0 type vcan
+ sudo ip link set dev vcan0 up
+
+The same commands can be executed from Scapy like this::
+
+ from scapy.layers.can import *
+ import os
+
+ bashCommand = "/bin/bash -c 'sudo modprobe vcan; sudo ip link add name vcan0 type vcan; sudo ip link set dev vcan0 up'"
+ os.system(bashCommand)
+
+If it's required, a CAN interface can be set into a ``listen-only`` or ``loopback`` mode with ``ip link set`` commands::
+
+ ip link set vcan0 type can help # shows additional information
+
+
+Linux can-utils
^^^^^^^^^^^^^^^
+As part of Linux SocketCAN, some very useful command line tools are provided from
+Oliver Hartkopp: https://github.com/linux-can/can-utils
+
+The following example shows the basic functions of Linux can-utils. These utilities
+are very handy for quick checks, dumping, sending, or logging of CAN messages
+from the command line.
+
+.. image:: ../graphics/animations/animation-cansend.svg
+
+Scapy CANSocket
+---------------
+
In Scapy, two kind of CANSockets are implemented. One implementation is called **Native CANSocket**,
the other implementation is called **Python-can CANSocket**.
@@ -338,71 +591,6 @@ Close the sockets::
.. image:: ../graphics/animations/animation-scapy-cansockets-mitm.svg
.. image:: ../graphics/animations/animation-scapy-cansockets-mitm2.svg
-DBC File Format and CAN Signals
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-In order to support the DBC file format, ``SignalFields`` and the ``SignalPacket``
-classes were added to Scapy. ``SignalFields`` should only be used inside a ``SignalPacket``.
-Multiplexer fields (MUX) can be created through ``ConditionalFields``. The following
-example demonstrates the usage::
-
- DBC Example:
-
- BO_ 4 muxTestFrame: 7 TEST_ECU
- SG_ myMuxer M : 53|3@1+ (1,0) [0|0] "" CCL_TEST
- SG_ muxSig4 m0 : 25|7@1- (1,0) [0|0] "" CCL_TEST
- SG_ muxSig3 m0 : 16|9@1+ (1,0) [0|0] "" CCL_TEST
- SG_ muxSig2 m0 : 15|8@0- (1,0) [0|0] "" CCL_TEST
- SG_ muxSig1 m0 : 0|8@1- (1,0) [0|0] "" CCL_TEST
- SG_ muxSig5 m1 : 22|7@1- (0.01,0) [0|0] "" CCL_TEST
- SG_ muxSig6 m1 : 32|9@1+ (2,10) [0|0] "mV" CCL_TEST
- SG_ muxSig7 m1 : 2|8@0- (0.5,0) [0|0] "" CCL_TEST
- SG_ muxSig8 m1 : 0|6@1- (10,0) [0|0] "" CCL_TEST
- SG_ muxSig9 : 40|8@1- (100,-5) [0|0] "V" CCL_TEST
-
- BO_ 3 testFrameFloat: 8 TEST_ECU
- SG_ floatSignal2 : 32|32@1- (1,0) [0|0] "" CCL_TEST
- SG_ floatSignal1 : 7|32@0- (1,0) [0|0] "" CCL_TEST
-
-Scapy implementation of this DBC description::
-
- class muxTestFrame(SignalPacket):
- fields_desc = [
- LEUnsignedSignalField("myMuxer", default=0, start=53, size=3),
- ConditionalField(LESignedSignalField("muxSig4", default=0, start=25, size=7), lambda p: p.myMuxer == 0),
- ConditionalField(LEUnsignedSignalField("muxSig3", default=0, start=16, size=9), lambda p: p.myMuxer == 0),
- ConditionalField(BESignedSignalField("muxSig2", default=0, start=15, size=8), lambda p: p.myMuxer == 0),
- ConditionalField(LESignedSignalField("muxSig1", default=0, start=0, size=8), lambda p: p.myMuxer == 0),
- ConditionalField(LESignedSignalField("muxSig5", default=0, start=22, size=7, scaling=0.01), lambda p: p.myMuxer == 1),
- ConditionalField(LEUnsignedSignalField("muxSig6", default=0, start=32, size=9, scaling=2, offset=10, unit="mV"), lambda p: p.myMuxer == 1),
- ConditionalField(BESignedSignalField("muxSig7", default=0, start=2, size=8, scaling=0.5), lambda p: p.myMuxer == 1),
- ConditionalField(LESignedSignalField("muxSig8", default=0, start=3, size=3, scaling=10), lambda p: p.myMuxer == 1),
- LESignedSignalField("muxSig9", default=0, start=41, size=7, scaling=100, offset=-5, unit="V"),
- ]
-
- class testFrameFloat(SignalPacket):
- fields_desc = [
- LEFloatSignalField("floatSignal2", default=0, start=32),
- BEFloatSignalField("floatSignal1", default=0, start=7)
- ]
-
- bind_layers(SignalHeader, muxTestFrame, identifier=0x123)
- bind_layers(SignalHeader, testFrameFloat, identifier=0x321)
-
- dbc_sock = CANSocket("can0", basecls=SignalHeader)
-
- pkt = SignalHeader()/testFrameFloat(floatSignal2=3.4)
-
- dbc_sock.send(pkt)
-
-This example uses the class ``SignalHeader`` as header. The payload is specified by individual ``SignalPackets``.
-``bind_layers`` combines the header with the payload dependent on the CAN identifier.
-If you want to directly receive ``SignalPackets`` from your ``CANSocket``, provide the parameter ``basecls`` to
-the ``init`` function of your ``CANSocket``.
-
-Canmatrix supports the creation of Scapy files from DBC or AUTOSAR XML files https://github.com/ebroecker/canmatrix
-
-
CAN Calibration Protocol (CCP)
==============================
@@ -479,41 +667,130 @@ If we are interested in the response of an Ecu, we need to set the basecls param
CANSocket to XCPonCAN and we need to use sr1:
Sending a CTO message::
- sock = CANSocket(bustype='socketcan', channel='vcan0', basecls=XCPonCAN)
- dto = sock.sr1(pkt)
+ sock = CANSocket(bustype='socketcan', channel='vcan0', basecls=XCPonCAN)
+ dto = sock.sr1(pkt)
+
+Since sr1 calls the answers function, our payload of the XCP-response objects gets interpreted with the
+command of our CTO object. Otherwise it could not be interpreted.
+The first message should always be the "CONNECT" message, the response of the Ecu determines how the messages are read. E.g.: byte order.
+Otherwise, one must set the address granularity, and max size of the DTOs and CTOs per hand in the contrib config::
+
+ conf.contribs['XCP']['Address_Granularity_Byte'] = 1 # Can be 1, 2 or 4
+ conf.contribs['XCP']['MAX_CTO'] = 8
+ conf.contribs['XCP']['MAX_DTO'] = 8
+
+If you do not want this to be set after receiving the message you can also disable that feature::
+
+ conf.contribs['XCP']['allow_byte_order_change'] = False
+ conf.contribs['XCP']['allow_ag_change'] = False
+ conf.contribs['XCP']['allow_cto_and_dto_change'] = False
+
+To send a pkt over TCP or UDP another header must be used.
+TCP::
+
+ prt1, prt2 = 12345, 54321
+ XCPOnTCP(sport=prt1, dport=prt2) / CTORequest() / Connect()
+
+UDP::
+
+ XCPOnUDP(sport=prt1, dport=prt2) / CTORequest() / Connect()
+
+
+XCPScanner
+---------------
+
+The XCPScanner is a utility to find the CAN identifiers of ECUs that support XCP.
+
+Commandline usage example::
+
+ python -m scapy.tools.automotive.xcpscanner -h
+ Finds XCP slaves using the "GetSlaveId"-message(Broadcast) or the "Connect"-message.
+
+ positional arguments:
+ channel Linux SocketCAN interface name, e.g.: vcan0
+
+ optional arguments:
+ -h, --help show this help message and exit
+ --start START, -s START
+ Start identifier CAN (in hex).
+ The scan will test ids between --start and --end (inclusive)
+ Default: 0x00
+ --end END, -e END End identifier CAN (in hex).
+ The scan will test ids between --start and --end (inclusive)
+ Default: 0x7ff
+ --sniff_time', '-t' Duration in milliseconds a sniff is waiting for a response.
+ Default: 100
+ --broadcast, -b Use Broadcast-message GetSlaveId instead of default "Connect"
+ (GetSlaveId is an optional Message that is not always implemented)
+ --verbose VERBOSE, -v
+ Display information during scan
+
+ Examples:
+ python3.6 -m scapy.tools.automotive.xcpscanner can0
+ python3.6 -m scapy.tools.automotive.xcpscanner can0 -b 500
+ python3.6 -m scapy.tools.automotive.xcpscanner can0 -s 50 -e 100
+ python3.6 -m scapy.tools.automotive.xcpscanner can0 -b 500 -v
+
+
+Interactive shell usage example::
+ >>> conf.contribs['CANSocket'] = {'use-python-can': False}
+ >>> load_layer("can")
+ >>> load_contrib("automotive.xcp.xcp")
+ >>> sock = CANSocket("vcan0")
+ >>> sock.basecls = XCPOnCAN
+ >>> scanner = XCPOnCANScanner(sock)
+ >>> result = scanner.start_scan()
+
+The result includes the slave_id (the identifier of the Ecu that receives XCP messages),
+and the response_id (the identifier that the Ecu will send XCP messages to).
+
+ISOTP
+=====
+
+ISOTP message
+-------------
+
+Creating an ISOTP message::
+
+ load_contrib('isotp')
+ ISOTP(src=0x241, dst=0x641, data=b"\x3eabc")
+
+Creating an ISOTP message with extended addressing::
+
+ ISOTP(src=0x241, dst=0x641, exdst=0x41, data=b"\x3eabc")
-Since sr1 calls the answers function, our payload of the XCP-response objects gets interpreted with the
-command of our CTO object. Otherwise it could not be interpreted.
-The first message should always be the "CONNECT" message, the response of the Ecu determines how the messages are read. E.g.: byte order.
-Otherwise, one must set the address granularity, and max size of the DTOs and CTOs per hand in the contrib config::
+Creating an ISOTP message with extended addressing::
- conf.contribs['XCP']['Address_Granularity_Byte'] = 1 # Can be 1, 2 or 4
- conf.contribs['XCP']['MAX_CTO'] = 8
- conf.contribs['XCP']['MAX_DTO'] = 8
+ ISOTP(src=0x241, dst=0x641, exdst=0x41, exsrc=0x41, data=b"\x3eabc")
-If you do not want this to be set after receiving the message you can also disable that feature::
+Create CAN-frames from an ISOTP message::
- conf.contribs['XCP']['allow_byte_order_change'] = False
- conf.contribs['XCP']['allow_ag_change'] = False
- conf.contribs['XCP']['allow_cto_and_dto_change'] = False
+ ISOTP(src=0x241, dst=0x641, exdst=0x41, exsrc=0x55, data=b"\x3eabc" * 10).fragment()
-To send a pkt over TCP or UDP another header must be used.
-TCP::
+Send ISOTP message over ISOTP socket::
- prt1, prt2 = 12345, 54321
- XCPOnTCP(sport=prt1, dport=prt2) / CTORequest() / Connect()
+ isoTpSocket = ISOTPSocket('vcan0', sid=0x241, did=0x641)
+ isoTpMessage = ISOTP('Message')
+ isoTpSocket.send(isoTpMessage)
-UDP::
+Sniff ISOTP message::
- XCPOnUDP(sport=prt1, dport=prt2) / CTORequest() / Connect()
+ isoTpSocket = ISOTPSocket('vcan0', sid=0x641, did=0x241)
+ packets = isoTpSocket.sniff(timeout=0.5)
+ISOTP Sockets
+-------------
+Scapy provides two kinds of ISOTP-Sockets. One implementation, the ``ISOTPNativeSocket``
+is using the Linux kernel module from Hartkopp. The other implementation, the ``ISOTPSoftSocket``
+is completely implemented in Python. This implementation can be used on Linux,
+Windows, and OSX.
-ISOTP
-=====
+An ``ISOTPSocket`` will not respect ``src, dst, exdst, exsrc`` of an ``ISOTP``
+message object.
System compatibilities
-----------------------
+^^^^^^^^^^^^^^^^^^^^^^
Dependent on your setup, different implementations have to be used.
@@ -535,43 +812,58 @@ The decision is made dependent on the configuration ``conf.contribs['ISOTP'] = {
This will allow you to write platform independent code. Apply this configuration before loading the ISOTP layer
with ``load_contrib('isotp')``.
-Another remark in respect to ISOTPSocket compatibility. Always use with for socket creation. Example::
+Another remark in respect to ISOTPSocket compatibility. Always use ``with`` for
+socket creation. This ensures that ``ISOTPSoftSocket`` objects will get closed
+properly.
+Example::
with ISOTPSocket("vcan0", did=0x241, sid=0x641) as sock:
sock.send(...)
+ISOTPNativeSocket
+^^^^^^^^^^^^^^^^^
+**Requires:**
-ISOTP message
--------------
+* Python3
+* Linux
+* Hartkopp's Linux kernel module: ``https://github.com/hartkopp/can-isotp.git`` (merged into mainline Linux in 5.10)
-Creating an ISOTP message::
+During pentests, the ISOTPNativeSockets has a better performance and
+reliability, usually. If you are working on Linux, consider this implementation::
+ conf.contribs['ISOTP'] = {'use-can-isotp-kernel-module': True}
load_contrib('isotp')
- ISOTP(src=0x241, dst=0x641, data=b"\x3eabc")
-
-Creating an ISOTP message with extended addressing::
-
- ISOTP(src=0x241, dst=0x641, exdst=0x41, data=b"\x3eabc")
+ sock = ISOTPSocket("can0", sid=0x641, did=0x241)
-Creating an ISOTP message with extended addressing::
+Since this implementation is using a standard Linux socket, all Scapy functions
+like ``sniff, sr, sr1, bridge_and_sniff`` work out of the box.
- ISOTP(src=0x241, dst=0x641, exdst=0x41, exsrc=0x41, data=b"\x3eabc")
+ISOTPSoftSocket
+^^^^^^^^^^^^^^^
-Create CAN-frames from an ISOTP message::
+ISOTPSoftSockets can use any CANSocket. This gives the flexibility to use all
+python-can interfaces. Additionally, these sockets work on Python2 and Python3.
+Usage on Linux with native CANSockets::
- ISOTP(src=0x241, dst=0x641, exdst=0x41, exsrc=0x55, data=b"\x3eabc" * 10).fragment()
+ conf.contribs['ISOTP'] = {'use-can-isotp-kernel-module': False}
+ load_contrib('isotp')
+ with ISOTPSocket("can0", sid=0x641, did=0x241) as sock:
+ sock.send(...)
-Send ISOTP message over ISOTP socket::
+Usage with python-can CANSockets::
- isoTpSocket = ISOTPSocket('vcan0', sid=0x241, did=0x641)
- isoTpMessage = ISOTP('Message')
- isoTpSocket.send(isoTpMessage)
+ conf.contribs['ISOTP'] = {'use-can-isotp-kernel-module': False}
+ conf.contribs['CANSocket'] = {'use-python-can': True}
+ load_contrib('isotp')
+ with ISOTPSocket(CANSocket(bustype='socketcan', channel="can0"), sid=0x641, did=0x241) as sock:
+ sock.send(...)
-Sniff ISOTP message::
+This second example allows the usage of any ``python_can.interface`` object.
- isoTpSocket = ISOTPSocket('vcan0', sid=0x641, did=0x241)
- packets = isoTpSocket.sniff(timeout=0.5)
+**Attention:** The internal implementation of ISOTPSoftSockets requires a background
+thread. In order to be able to close this thread properly, we suggest the use of
+Pythons ``with`` statement.
ISOTP MITM attack with bridge and sniff
---------------------------------------
@@ -584,15 +876,6 @@ Set up two vcans on Linux terminal::
sudo ip link set dev vcan0 up
sudo ip link set dev vcan1 up
-Set up ISOTP:
-
-First make sure you installed an iso-tp kernel module.
-
-When the vcan core module is loaded with "sudo modprobe vcan" the iso-tp module can be loaded to the kernel.
-
-Therefore navigate to isotp directory, and load module with "sudo insmod ./net/can/can-isotp.ko". (Tested on Kernel 4.9.135-1-MANJARO)
-
-Detailed instructions you find in https://github.com/hartkopp/can-isotp.
Import modules::
@@ -632,7 +915,7 @@ Create threads for sending packet and to bridge and sniff::
threadBridge = threading.Thread(target=bridge)
threadSender = threading.Thread(target=sendPacketWithISOTPSocket)
-Start threads are based on Linux kernel modules. The python-can project is used to support CAN and CANSockets on other systems, besides Linux. This guide explains the hardware setup on a BeagleBone Black. The BeagleBone Black was chosen because of its two CAN interfaces on the main processor. The presence of two CAN interfaces in one device gives the possibility of CAN MITM attacks and session hijacking. The Cannelloni framework turns a BeagleBone Black into a CAN-to-UDP interface, which gives you the freedom to run Scapy on a more powerful machine.::
+Start threads::
threadBridge.start()
threadSender.start()
@@ -646,61 +929,6 @@ Close sockets::
isoTpSocketVCan0.close()
isoTpSocketVCan1.close()
-An ISOTPSocket will not respect ``src, dst, exdst, exsrc`` of an ISOTP message object.
-
-ISOTP Sockets
-=============
-
-Scapy provides two kinds of ISOTP Sockets. One implementation, the ISOTPNativeSocket
-is using the Linux kernel module from Hartkopp. The other implementation, the ISOTPSoftSocket
-is completely implemented in Python. This implementation can be used on Linux,
-Windows, and OSX.
-
-ISOTPNativeSocket
------------------
-
-**Requires:**
-
-* Python3
-* Linux
-* Hartkopp's Linux kernel module: ``https://github.com/hartkopp/can-isotp.git``
-
-During pentests, the ISOTPNativeSockets has a better performance and
-reliability, usually. If you are working on Linux, consider this implementation::
-
- conf.contribs['ISOTP'] = {'use-can-isotp-kernel-module': True}
- load_contrib('isotp')
- sock = ISOTPSocket("can0", sid=0x641, did=0x241)
-
-Since this implementation is using a standard Linux socket, all Scapy functions
-like ``sniff, sr, sr1, bridge_and_sniff`` work out of the box.
-
-ISOTPSoftSocket
----------------
-
-ISOTPSoftSockets can use any CANSocket. This gives the flexibility to use all
-python-can interfaces. Additionally, these sockets work on Python2 and Python3.
-Usage on Linux with native CANSockets::
-
- conf.contribs['ISOTP'] = {'use-can-isotp-kernel-module': False}
- load_contrib('isotp')
- with ISOTPSocket("can0", sid=0x641, did=0x241) as sock:
- sock.send(...)
-
-Usage with python-can CANSockets::
-
- conf.contribs['ISOTP'] = {'use-can-isotp-kernel-module': False}
- conf.contribs['CANSocket'] = {'use-python-can': True}
- load_contrib('isotp')
- with ISOTPSocket(CANSocket(bustype='socketcan', channel="can0"), sid=0x641, did=0x241) as sock:
- sock.send(...)
-
-This second example allows the usage of any ``python_can.interface`` object.
-
-**Attention:** The internal implementation of ISOTPSoftSockets requires a background
-thread. In order to be able to close this thread properly, we suggest the use of
-Pythons ``with`` statement.
-
ISOTPScan and ISOTPScanner
--------------------------
@@ -770,56 +998,6 @@ Interactive shell usage example::
<<ISOTPNativeSocket: read/write packets at a given CAN interface using CAN_ISOTP socket > at 0x7f98f912e950>,
<<ISOTPNativeSocket: read/write packets at a given CAN interface using CAN_ISOTP socket > at 0x7f98f906c0d0>]
-XCPScanner
----------------
-
-The XCPScanner is a utility to find the CAN identifiers of ECUs that support XCP.
-
-Commandline usage example::
-
- python -m scapy.tools.automotive.xcpscanner -h
- Finds XCP slaves using the "GetSlaveId"-message(Broadcast) or the "Connect"-message.
-
- positional arguments:
- channel Linux SocketCAN interface name, e.g.: vcan0
-
- optional arguments:
- -h, --help show this help message and exit
- --start START, -s START
- Start identifier CAN (in hex).
- The scan will test ids between --start and --end (inclusive)
- Default: 0x00
- --end END, -e END End identifier CAN (in hex).
- The scan will test ids between --start and --end (inclusive)
- Default: 0x7ff
- --sniff_time', '-t' Duration in milliseconds a sniff is waiting for a response.
- Default: 100
- --broadcast, -b Use Broadcast-message GetSlaveId instead of default "Connect"
- (GetSlaveId is an optional Message that is not always implemented)
- --verbose VERBOSE, -v
- Display information during scan
-
- Examples:
- python3.6 -m scapy.tools.automotive.xcpscanner can0
- python3.6 -m scapy.tools.automotive.xcpscanner can0 -b 500
- python3.6 -m scapy.tools.automotive.xcpscanner can0 -s 50 -e 100
- python3.6 -m scapy.tools.automotive.xcpscanner can0 -b 500 -v
-
-
-Interactive shell usage example::
- >>> conf.contribs['CANSocket'] = {'use-python-can': False}
- >>> load_layer("can")
- >>> load_contrib("automotive.xcp.xcp")
- >>> sock = CANSocket("vcan0")
- >>> sock.basecls = XCPOnCAN
- >>> scanner = XCPOnCANScanner(sock)
- >>> result = scanner.start_scan()
-
-The result includes the slave_id (the identifier of the Ecu that receives XCP messages),
-and the response_id (the identifier that the Ecu will send XCP messages to).
-
-
-
UDS
===
@@ -878,7 +1056,8 @@ Customization example::
UDS_RDBI.dataIdentifiers[0x172b] = 'GatewayIP'
-If one wants to work with this custom additions, these can be loaded at runtime to the Scapy interpreter::
+If one wants to work with this custom additions, these can be loaded at runtime
+to the Scapy interpreter::
>>> load_contrib('automotive.uds')
>>> load_contrib('automotive.OEM-XYZ.car-model-xyz')
@@ -904,6 +1083,7 @@ If one wants to work with this custom additions, these can be loaded at runtime
GMLAN
=====
+
GMLAN is very similar to UDS. It's GMs application layer protocol for
flashing, calibration and diagnostic of their cars.
Use the argument ``basecls=GMLAN`` on the ``init`` function of an ISOTPSocket.
@@ -922,7 +1102,10 @@ This utility depends heavily on the support of the used protocol. ``UDS`` is sup
Log all commands applied to an Ecu
----------------------------------
-This example shows the logging mechanism of an Ecu object. The log of an Ecu is a dictionary of applied UDS commands. The key for this dictionary is the UDS service name. The value consists of a list of tuples, containing a timestamp and a log value
+This example shows the logging mechanism of an Ecu object. The log of an Ecu
+is a dictionary of applied UDS commands. The key for this dictionary is the
+UDS service name. The value consists of a list of tuples, containing a timestamp
+and a log value
Usage example::
@@ -936,7 +1119,9 @@ Usage example::
Trace all commands applied to an Ecu
------------------------------------
-This example shows the trace mechanism of an Ecu object. Traces of the current state of the Ecu object and the received message are printed on stdout. Some messages, depending on the protocol, will change the internal state of the Ecu.
+This example shows the trace mechanism of an Ecu object. Traces of the current
+state of the Ecu object and the received message are printed on stdout.
+Some messages, depending on the protocol, will change the internal state of the Ecu.
Usage example::
@@ -965,7 +1150,10 @@ Usage example::
Analyze multiple UDS messages
-----------------------------
-This example shows how to load ``UDS`` messages from a ``.pcap`` file containing ``CAN`` messages. A ``PcapReader`` object is used as socket and an ``ISOTPSession`` parses ``CAN`` frames to ``ISOTP`` frames which are then casted to ``UDS`` objects through the ``basecls`` parameter
+This example shows how to load ``UDS`` messages from a ``.pcap`` file containing
+``CAN`` messages. A ``PcapReader`` object is used as socket and an
+``ISOTPSession`` parses ``CAN`` frames to ``ISOTP`` frames which are
+then casted to ``UDS`` objects through the ``basecls`` parameter
Usage example::
@@ -984,7 +1172,11 @@ Usage example::
Analyze on the fly with EcuSession
----------------------------------
-This example shows the usage of an EcuSession in sniff. An ISOTPSocket or any socket like object which returns entire messages of the right protocol can be used. An ``EcuSession`` is used as supersession in an ``ISOTPSession``. To obtain the ``Ecu`` object from an ``EcuSession``, the ``EcuSession`` has to be created outside of sniff.
+This example shows the usage of an EcuSession in sniff. An ISOTPSocket or any
+socket like object which returns entire messages of the right protocol can be
+used. An ``EcuSession`` is used as supersession in an ``ISOTPSession``.
+To obtain the ``Ecu`` object from an ``EcuSession``, the ``EcuSession``
+has to be created outside of sniff.
Usage example::
@@ -1005,7 +1197,10 @@ SOME/IP and SOME/IP SD messages
Creating a SOME/IP message
--------------------------
-This example shows a SOME/IP message which requests a service 0x1234 with the method 0x421. Different types of SOME/IP messages follow the same procedure and their specifications can be seen here ``http://www.some-ip.com/papers/cache/AUTOSAR_TR_SomeIpExample_4.2.1.pdf``.
+This example shows a SOME/IP message which requests a service 0x1234 with the
+method 0x421. Different types of SOME/IP messages follow the same procedure
+and their specifications can be seen here
+``http://www.some-ip.com/papers/cache/AUTOSAR_TR_SomeIpExample_4.2.1.pdf``.
Load the contribution::
@@ -1095,9 +1290,6 @@ Stack it and send it::
OBD
===
-OBD message
------------
-
OBD is implemented on top of ISOTP. Use an ISOTPSocket for the communication with an Ecu.
You should set the parameters ``basecls=OBD`` and ``padding=True`` in your ISOTPSocket init call.
@@ -1120,8 +1312,9 @@ The response will contain a PacketListField, called `data_records`. This field c
|###[ PID_00_PIDsSupported ]###
| supported_pids= PID20+PID1F+PID1C+PID15+PID14+PID13+PID11+PID10+PID0F+PID0E+PID0D+PID0C+PID0B+PID0A+PID07+PID06+PID05+PID04+PID03+PID01
+
Let's assume our Ecu under test supports the pid 0x15::
-
+
req = OBD()/OBD_S01(pid=[0x15])
resp = sock.sr1(req)
resp.show()
@@ -1146,7 +1339,7 @@ Service 08 supports Test Identifiers (tid).
Service 09 supports Information Identifiers (iid).
Examples:
-^^^^^^^^^
+---------
Request supported Information Identifiers::
@@ -1174,234 +1367,6 @@ Request the Vehicle Identification Number (VIN)::
Test-Setup Tutorials
====================
-Hardware Setup
---------------
-
-Beagle Bone Black Operating System Setup
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-#. | **Download an Image**
- | The latest Debian Linux image can be found at the website
- | ``https://beagleboard.org/latest-images``. Choose the BeagleBone
- Black IoT version and download it.
-
- ::
-
- wget https://debian.beagleboard.org/images/bone-debian-8.7\
- -iot-armhf-2017-03-19-4gb.img.xz
-
-
- After the download, copy it to an SD-Card with minimum of 4 GB storage.
-
- ::
-
- xzcat bone-debian-8.7-iot-armhf-2017-03-19-4gb.img.xz | \
- sudo dd of=/dev/xvdj
-
-
-#. | **Enable WiFi**
- | USB-WiFi dongles are well supported by Debian Linux. Login over SSH
- on the BBB and add the WiFi network credentials to the file
- ``/var/lib/connman/wifi.config``. If a USB-WiFi dongle is not
- available, it is also possible to share the host's internet
- connection with the Ethernet connection of the BBB emulated over
- USB. A tutorial to share the host network connection can be found
- on this page:
- | ``https://elementztechblog.wordpress.com/2014/12/22/sharing-internet -using-network-over-usb-in-beaglebone-black/``.
- | Login as root onto the BBB:
-
- ::
-
- ssh debian@192.168.7.2
- sudo su
-
-
- Provide the WiFi login credentials to connman:
-
- ::
-
- echo "[service_home]
- Type = wifi
- Name = ssid
- Security = wpa
- Passphrase = xxxxxxxxxxxxx" \
- > /var/lib/connman/wifi.config
-
-
- Restart the connman service:
-
- ::
-
- systemctl restart connman.service
-
-
-Dual-CAN Setup
-^^^^^^^^^^^^^^
-
-#. | **Device tree setup**
- | You'll need to follow this section only if you want to use two CAN
- interfaces (DCAN0 and DCAN1). This will disable I2C2 from using pins
- P9.19 and P9.20, which are needed by DCAN0. You only need to perform the
- steps in this section once.
-
- | Warning: The configuration in this section will disable BBB capes from
- working. Each cape has a small I2C EEPROM that stores info that the BBB
- needs to know in order to communicate with the cape. Disable I2C2, and
- the BBB has no way to talk to cape EEPROMs. Of course, if you don't use
- capes then this is not a problem.
-
- | Acquire DTS sources that matches your kernel version. Go
- `here <https://github.com/beagleboard/linux/>`__ and switch over to the
- branch that represents your kernel version. Download the entire branch
- as a ZIP file. Extract it and do the following (version 4.1 shown as an
- example):
-
- ::
-
- # cd ~/src/linux-4.1/arch/arm/boot/dts/include/
- # rm dt-bindings
- # ln -s ../../../../../include/dt-bindings
- # cd ..
- Edit am335x-bone-common.dtsi and ensure the line with "//pinctrl-0 = <&i2c2_pins>;" is commented out.
- Remove the complete &ocp section at the end of this file
- # mv am335x-boneblack.dts am335x-boneblack.raw.dts
- # cpp -nostdinc -I include -undef -x assembler-with-cpp am335x-boneblack.raw.dts > am335x-boneblack.dts
- # dtc -W no-unit_address_vs_reg -O dtb -o am335x-boneblack.dtb -b 0 -@ am335x-boneblack.dts
- # cp /boot/dtbs/am335x-boneblack.dtb /boot/dtbs/am335x-boneblack.orig.dtb
- # cp am335x-boneblack.dtb /boot/dtbs/
- Reboot
-
-#. **Overlay setup**
- | This section describes how to build the device overlays for the two CAN devices (DCAN0 and DCAN1). You only need to perform the steps in this section once.
- | Acquire BBB cape overlays, in one of two ways…
-
- ::
-
- # apt-get install bb-cape-overlays
- https://github.com/beagleboard/bb.org-overlays/
-
- | Then do the following:
-
-
- ::
-
- # cd ~/src/bb.org-overlays-master/src/arm
- # ln -s ../../include
- # mv BB-CAN1-00A0.dts BB-CAN1-00A0.raw.dts
- # cp BB-CAN1-00A0.raw.dts BB-CAN0-00A0.raw.dts
- Edit BB-CAN0-00A0.raw.dts and make relevant to CAN0. Example is shown below.
- # cpp -nostdinc -I include -undef -x assembler-with-cpp BB-CAN0-00A0.raw.dts > BB-CAN0-00A0.dts
- # cpp -nostdinc -I include -undef -x assembler-with-cpp BB-CAN1-00A0.raw.dts > BB-CAN1-00A0.dts
- # dtc -W no-unit_address_vs_reg -O dtb -o BB-CAN0-00A0.dtbo -b 0 -@ BB-CAN0-00A0.dts
- # dtc -W no-unit_address_vs_reg -O dtb -o BB-CAN1-00A0.dtbo -b 0 -@ BB-CAN1-00A0.dts
- # cp *.dtbo /lib/firmware
-
-
-#. | **CAN0 Example Overlay**
- | Inside the DTS folder, create a file with the content of the
- following listing.
-
- ::
-
- cd ~/bb.org-overlays/src/arm
- cat <<EOF > BB-CAN0-00A0.raw.dts
-
- /*
- * Copyright (C) 2015 Robert Nelson <robertcnelson@gmail.com>
- *
- * Virtual cape for CAN0 on connector pins P9.19 P9.20
- *
- * This program is free software; you can redistribute it and/or modify
- * it under the terms of the GNU General Public License version 2 as
- * published by the Free Software Foundation.
- */
- /dts-v1/;
- /plugin/;
-
- #include <dt-bindings/board/am335x-bbw-bbb-base.h>
- #include <dt-bindings/pinctrl/am33xx.h>
-
- / {
- compatible = "ti,beaglebone", "ti,beaglebone-black", "ti,beaglebone-green";
-
- /* identification */
- part-number = "BB-CAN0";
- version = "00A0";
-
- /* state the resources this cape uses */
- exclusive-use =
- /* the pin header uses */
- "P9.19", /* can0_rx */
- "P9.20", /* can0_tx */
- /* the hardware ip uses */
- "dcan0";
-
- fragment@0 {
- target = <&am33xx_pinmux>;
- __overlay__ {
- bb_dcan0_pins: pinmux_dcan0_pins {
- pinctrl-single,pins = <
- BONE_P9_19 (PIN_INPUT_PULLUP | MUX_MODE2) /* uart1_txd.d_can0_rx */
- BONE_P9_20 (PIN_OUTPUT_PULLUP | MUX_MODE2) /* uart1_rxd.d_can0_tx */
- >;
- };
- };
- };
-
- fragment@1 {
- target = <&dcan0>;
- __overlay__ {
- status = "okay";
- pinctrl-names = "default";
- pinctrl-0 = <&bb_dcan0_pins>;
- };
- };
- };
- EOF
-
-
-#. | **Test the Dual-CAN Setup**
- | Do the following each time you need CAN, or automate these steps if you like.
-
- ::
-
- # echo BB-CAN0 > /sys/devices/platform/bone_capemgr/slots
- # echo BB-CAN1 > /sys/devices/platform/bone_capemgr/slots
- # modprobe can
- # modprobe can-dev
- # modprobe can-raw
- # ip link set can0 up type can bitrate 50000
- # ip link set can1 up type can bitrate 50000
-
- Check the output of the Capemanager if both CAN interfaces have been
- loaded.
-
- ::
-
- cat /sys/devices/platform/bone_capemgr/slots
-
- 0: PF---- -1
- 1: PF---- -1
- 2: PF---- -1
- 3: PF---- -1
- 4: P-O-L- 0 Override Board Name,00A0,Override Manuf, BB-CAN0
- 5: P-O-L- 1 Override Board Name,00A0,Override Manuf, BB-CAN1
-
-
- If something went wrong, ``dmesg`` provides kernel messages to analyse the root of failure.
-
-#. | **References**
-
- - `embedded-things.com: Enable CANbus on the Beaglebone
- Black <http://www.embedded-things.com/bbb/enable-canbus-on-the-beaglebone-black/>`__
- - `electronics.stackexchange.com: Beaglebone Black CAN bus
- Setup <https://electronics.stackexchange.com/questions/195416/beaglebone-black-can-bus-setup>`__
-
-#. | **Acknowledgment**
- | Thanks to Tom Haramori. Parts of this section are copied from his guide: https://github.com/haramori/rhme3/blob/master/Preparation/BBB_CAN_setup.md
-
-
-
ISO-TP Kernel Module Installation
---------------------------------
@@ -1410,16 +1375,16 @@ A Linux ISO-TP kernel module can be downloaded from this website:
``README.isotp`` in this repository provides all information and
necessary steps for downloading and building this kernel module. The
ISO-TP kernel module should also be added to the ``/etc/modules`` file,
-to load this module automatically at system boot of the BBB.
+to load this module automatically at system boot.
CAN-Interface Setup
-------------------
-As the final step to prepare the BBB's CAN interfaces for usage, these
+As the final step to prepare CAN interfaces for usage, these
interfaces have to be set up through some terminal commands. The bitrate
can be chosen to fit the bitrate of a CAN bus under test.
-::
+How-To::
ip link set can0 up type can bitrate 500000
ip link set can1 up type can bitrate 500000
@@ -1457,11 +1422,8 @@ To build a small test environment in which you can send SOME/IP messages to and
-Software Setup
---------------
-
-Cannelloni Framework Installation
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Cannelloni Framework
+--------------------
The Cannelloni framework is a small application written in C++ to
transfer CAN data over UDP. In this way, a researcher can map the CAN
@@ -1473,7 +1435,7 @@ explains the installation and usage in detail. Cannelloni needs virtual
CAN interfaces on the operator's machine. The next listing shows the
setup of virtual CAN interfaces.
-::
+How-To::
modprobe vcan
|
Pyomo__pyomo-56 | XPRESS Solver Error : AttributeError: 'NoneType' object has no attribute 'problem'
I've pasted the traceback below :
```
Traceback (most recent call last):
File "model.py", line 15, in <module>
solver.solve(model)
File "/Users/$USER/anaconda/lib/python2.7/site-packages/pyomo/opt/base/solvers.py", line 587, in solve
result = self._postsolve()
File "/Users/$USER/anaconda/lib/python2.7/site-packages/pyomo/opt/solver/shellcmd.py", line 267, in _postsolve
results = self.process_output(self._rc)
File "/Users/$USER/anaconda/lib/python2.7/site-packages/pyomo/opt/solver/shellcmd.py", line 329, in process_output
self.process_soln_file(results)
File "/Users/$USER/anaconda/lib/python2.7/site-packages/pyomo/solvers/plugins/solvers/XPRESS.py", line 336, in process_soln_file
results.problem.number_of_objectives=1
AttributeError: 'NoneType' object has no attribute 'problem'
```
| [
{
"content": "# _________________________________________________________________________\n#\n# Pyomo: Python Optimization Modeling Objects\n# Copyright (c) 2014 Sandia Corporation.\n# Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation,\n# the U.S. Government retains certain rights in th... | [
{
"content": "# _________________________________________________________________________\n#\n# Pyomo: Python Optimization Modeling Objects\n# Copyright (c) 2014 Sandia Corporation.\n# Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation,\n# the U.S. Government retains certain rights in th... | diff --git a/pyomo/solvers/plugins/solvers/XPRESS.py b/pyomo/solvers/plugins/solvers/XPRESS.py
index 143c6ffc2dc..929272d6efc 100644
--- a/pyomo/solvers/plugins/solvers/XPRESS.py
+++ b/pyomo/solvers/plugins/solvers/XPRESS.py
@@ -198,8 +198,6 @@ def process_logfile(self):
log_file_contents = "".join(log_file.readlines())
log_file.close()
- return
-
for line in log_file_contents.split("\n"):
tokens = re.split('[ \t]+',line.strip())
|
Qiskit__qiskit-6171 | num_qubits() for DictStateFn is inefficient
To get the number of qubits, a list of all keys in the dictionary is constructed. But, only the length of the first key is used. Constructing the entire list is wasteful.
https://github.com/Qiskit/qiskit-terra/blob/c3b2d7acb80fa89043e6f38efb501275ec296616/qiskit/opflow/state_fns/dict_state_fn.py#L82
This code should work:
```python
len(next(iter(self.primitive)))
```
`%timeit` shows that the latter is faster even when the dict contains only two keys.
- **Qiskit Terra version**: 123d829ac, Feb 3 master
| [
{
"content": "# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2020, 2021.\n#\n# This code is licensed under the Apache License, Version 2.0. You may\n# obtain a copy of this license in the LICENSE.txt file in the root directory\n# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\n#\n... | [
{
"content": "# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2020, 2021.\n#\n# This code is licensed under the Apache License, Version 2.0. You may\n# obtain a copy of this license in the LICENSE.txt file in the root directory\n# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\n#\n... | diff --git a/qiskit/opflow/state_fns/dict_state_fn.py b/qiskit/opflow/state_fns/dict_state_fn.py
index a6a313f02815..73507140f0cc 100644
--- a/qiskit/opflow/state_fns/dict_state_fn.py
+++ b/qiskit/opflow/state_fns/dict_state_fn.py
@@ -81,7 +81,7 @@ def primitive_strings(self) -> Set[str]:
@property
def num_qubits(self) -> int:
- return len(list(self.primitive.keys())[0])
+ return len(next(iter(self.primitive)))
def add(self, other: OperatorBase) -> OperatorBase:
if not self.num_qubits == other.num_qubits:
|
kornia__kornia-2610 | bug in ycbcr_to_rgb function
### Describe the bug
https://github.com/kornia/kornia/blob/2c084f8dc108b3f0f3c8983ac3f25bf88638d01a/kornia/color/ycbcr.py#L70
#### now:
return torch.stack([r, g, b], -3)
#### need to be:
return torch.stack([r, g, b], -3).clamp(0,1)
#### because:
| [
{
"content": "import torch\nfrom torch import Tensor, nn\n\n\ndef _rgb_to_y(r: Tensor, g: Tensor, b: Tensor) -> Tensor:\n y: Tensor = 0.299 * r + 0.587 * g + 0.114 * b\n return y\n\n\ndef rgb_to_ycbcr(image: Tensor) -> Tensor:\n r\"\"\"Convert an RGB image to YCbCr.\n\n .. image:: _static/img/rgb_to... | [
{
"content": "import torch\nfrom torch import Tensor, nn\n\n\ndef _rgb_to_y(r: Tensor, g: Tensor, b: Tensor) -> Tensor:\n y: Tensor = 0.299 * r + 0.587 * g + 0.114 * b\n return y\n\n\ndef rgb_to_ycbcr(image: Tensor) -> Tensor:\n r\"\"\"Convert an RGB image to YCbCr.\n\n .. image:: _static/img/rgb_to... | diff --git a/kornia/color/ycbcr.py b/kornia/color/ycbcr.py
index 4e9fe5d856..c0cb597de7 100644
--- a/kornia/color/ycbcr.py
+++ b/kornia/color/ycbcr.py
@@ -98,7 +98,7 @@ def ycbcr_to_rgb(image: Tensor) -> Tensor:
r: Tensor = y + 1.403 * cr_shifted
g: Tensor = y - 0.714 * cr_shifted - 0.344 * cb_shifted
b: Tensor = y + 1.773 * cb_shifted
- return torch.stack([r, g, b], -3)
+ return torch.stack([r, g, b], -3).clamp(0, 1)
class RgbToYcbcr(nn.Module):
diff --git a/test/color/test_ycbcr.py b/test/color/test_ycbcr.py
index db513014fb..c291998296 100644
--- a/test/color/test_ycbcr.py
+++ b/test/color/test_ycbcr.py
@@ -185,25 +185,25 @@ def test_unit(self, device, dtype):
[
[
[
- [1.3226931, 0.5639256, 0.14902398, 1.0217545, 0.2569923],
- [0.37973762, 0.64386904, 1.1992811, -0.603531, 0.4239992],
- [1.0080798, 1.3131194, -0.1370286, 0.60653293, 0.3505922],
- [-0.4573757, 0.6076593, 0.33508536, 0.27470887, 1.2284023],
- [0.52727354, -0.1673561, 0.05404532, 1.2725366, 0.5783674],
+ [1.0000, 0.5639256, 0.14902398, 1.0000, 0.2569923],
+ [0.37973762, 0.64386904, 1.0000, 0.0000, 0.4239992],
+ [1.0000, 1.0000, 0.0000, 0.60653293, 0.3505922],
+ [0.0000, 0.6076593, 0.33508536, 0.27470887, 1.0000],
+ [0.52727354, 0.0000, 0.05404532, 1.0000, 0.5783674],
],
[
- [0.736647, -0.01305042, 0.55139434, 0.44206098, 0.4548782],
- [-0.22063601, 0.98196536, 0.54739904, 0.33826917, 0.850068],
- [0.72412336, 0.6222996, 0.110618, 1.0039049, 0.614918],
+ [0.736647, 0.0000, 0.55139434, 0.44206098, 0.4548782],
+ [0.0000, 0.98196536, 0.54739904, 0.33826917, 0.850068],
+ [0.72412336, 0.6222996, 0.110618, 1.0000, 0.614918],
[0.15862459, 0.0699634, 0.66296846, 0.4845066, 0.3705502],
- [1.0145653, 0.46070462, 0.5654058, 0.24897486, -0.11174999],
+ [1.0000, 0.46070462, 0.5654058, 0.24897486, 0.0000],
],
[
- [1.4161384, 0.88769174, 0.4394987, -0.31889397, -0.5671302],
- [0.77483994, 0.99839956, 1.6813064, 0.41622213, 1.3508832],
- [0.7488585, -0.04955059, 0.01748962, 0.9683316, 0.49795526],
- [0.9473541, 1.2473994, -0.3918787, 0.47037587, 1.2893858],
- [1.4082898, -0.21875012, 0.6804801, 0.9795798, 0.24646705],
+ [1.0000, 0.88769174, 0.4394987, 0.0000, 0.0000],
+ [0.77483994, 0.99839956, 1.0000, 0.41622213, 1.0000],
+ [0.7488585, 0.0000, 0.01748962, 0.9683316, 0.49795526],
+ [0.9473541, 1.0000, 0.0000, 0.47037587, 1.0000],
+ [1.0000, 0.0000, 0.6804801, 0.9795798, 0.24646705],
],
]
],
diff --git a/test/geometry/camera/test_pinhole.py b/test/geometry/camera/test_pinhole.py
index da890cc8e4..54b111ce80 100644
--- a/test/geometry/camera/test_pinhole.py
+++ b/test/geometry/camera/test_pinhole.py
@@ -198,6 +198,7 @@ def test_consistency(self, batch_size, device, dtype):
assert_close(pixel_coords_concat, pixel_coords_input, atol=1e-4, rtol=1e-4)
@pytest.mark.parametrize("batch_size", (1,))
+ @pytest.mark.slow
def test_gradcheck(self, batch_size, device, dtype):
H, W = 10, 20
fx, fy = W, H
diff --git a/test/nerf/test_nerf_solver.py b/test/nerf/test_nerf_solver.py
index bf856d2dde..bdcaba8f2b 100755
--- a/test/nerf/test_nerf_solver.py
+++ b/test/nerf/test_nerf_solver.py
@@ -26,6 +26,7 @@ def test_parameter_change_after_one_epoch(self, device, dtype):
for param_before_update, param_after_update in zip(params_before_update, params_after_update)
)
+ @pytest.mark.slow
def test_only_red_uniform_sampling(self, device, dtype):
torch.manual_seed(1) # For reproducibility of random processes
camera = create_one_camera(5, 9, device, dtype)
|
cookiecutter__cookiecutter-539 | Increase development status to 'beta' or 'stable'.
I think we can say the project is waaaay beyond alpha. :wink:
| [
{
"content": "#!/usr/bin/env python\n\nimport os\nimport sys\n\ntry:\n from setuptools import setup\nexcept ImportError:\n from distutils.core import setup\n\nversion = \"1.1.0\"\n\nif sys.argv[-1] == 'publish':\n os.system('python setup.py sdist upload')\n os.system('python setup.py bdist_wheel upl... | [
{
"content": "#!/usr/bin/env python\n\nimport os\nimport sys\n\ntry:\n from setuptools import setup\nexcept ImportError:\n from distutils.core import setup\n\nversion = \"1.1.0\"\n\nif sys.argv[-1] == 'publish':\n os.system('python setup.py sdist upload')\n os.system('python setup.py bdist_wheel upl... | diff --git a/setup.py b/setup.py
index 2d504822b..db893b12e 100755
--- a/setup.py
+++ b/setup.py
@@ -66,7 +66,7 @@
license='BSD',
zip_safe=False,
classifiers=[
- 'Development Status :: 3 - Alpha',
+ 'Development Status :: 5 - Production/Stable',
'Environment :: Console',
'Intended Audience :: Developers',
'Natural Language :: English',
|
ivy-llc__ivy-13425 | normal
| [
{
"content": "import ivy\nfrom ivy.func_wrapper import with_supported_dtypes\nfrom ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back\n\ntry:\n from torch import Generator\nexcept ImportError:\n from types import SimpleNamespace\n\n Generator = SimpleNamespace\n\n\ndef seed() -> ... | [
{
"content": "import ivy\nfrom ivy.func_wrapper import with_supported_dtypes\nfrom ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back\n\ntry:\n from torch import Generator\nexcept ImportError:\n from types import SimpleNamespace\n\n Generator = SimpleNamespace\n\n\ndef seed() -> ... | diff --git a/ivy/functional/frontends/torch/random_sampling.py b/ivy/functional/frontends/torch/random_sampling.py
index a8373e819e42a..c830a6863baaa 100644
--- a/ivy/functional/frontends/torch/random_sampling.py
+++ b/ivy/functional/frontends/torch/random_sampling.py
@@ -76,6 +76,20 @@ def rand(
)
+@with_supported_dtypes(
+ {
+ "1.11.0 and below": (
+ "float32",
+ "float64",
+ )
+ },
+ "torch",
+)
+@to_ivy_arrays_and_back
+def normal(mean, std, *, generator=None, out=None):
+ return ivy.random_normal(mean=mean, std=std, out=out)
+
+
@to_ivy_arrays_and_back
def rand_like(
input,
diff --git a/ivy_tests/test_ivy/test_frontends/test_torch/test_random_sampling.py b/ivy_tests/test_ivy/test_frontends/test_torch/test_random_sampling.py
index cc0ebd8fbb15e..2adc45cb57022 100644
--- a/ivy_tests/test_ivy/test_frontends/test_torch/test_random_sampling.py
+++ b/ivy_tests/test_ivy/test_frontends/test_torch/test_random_sampling.py
@@ -164,6 +164,62 @@ def call():
assert u.shape == v.shape
+@handle_frontend_test(
+ fn_tree="torch.normal",
+ dtype_and_mean=helpers.dtype_and_values(
+ available_dtypes=helpers.get_dtypes("float"),
+ min_value=-1000,
+ max_value=1000,
+ min_num_dims=1,
+ max_num_dims=5,
+ min_dim_size=2,
+ ),
+ dtype_and_std=helpers.dtype_and_values(
+ available_dtypes=helpers.get_dtypes("float"),
+ min_value=0,
+ max_value=1000,
+ min_num_dims=1,
+ max_num_dims=5,
+ min_dim_size=2,
+ ),
+)
+def test_torch_normal(
+ *,
+ dtype_and_mean,
+ dtype_and_std,
+ on_device,
+ fn_tree,
+ frontend,
+ test_flags,
+):
+ mean_dtype, mean = dtype_and_mean
+ _, std = dtype_and_std
+
+ def call():
+ return helpers.test_frontend_function(
+ input_dtypes=mean_dtype,
+ frontend=frontend,
+ test_flags=test_flags,
+ fn_tree=fn_tree,
+ on_device=on_device,
+ test_values=False,
+ mean=mean[0],
+ std=std[0],
+ )
+
+ ret = call()
+
+ if not ivy.exists(ret):
+ return
+
+ ret_np, ret_from_np = ret
+ ret_np = helpers.flatten_and_to_np(ret=ret_np)
+ ret_from_np = helpers.flatten_and_to_np(ret=ret_from_np)
+ for (u, v) in zip(ret_np, ret_from_np):
+ assert u.dtype == v.dtype
+ assert u.shape == v.shape
+
+
@handle_frontend_test(
fn_tree="torch.rand_like",
dtype=helpers.get_dtypes("float", full=False),
|
hpcaitech__ColossalAI-3323 | [tensor] fix some unittests
[tensor] fix some unittests
[tensor] fix some unittests
| [
{
"content": "from typing import List, Union, Any\nfrom ..proxy import ColoProxy, ColoAttribute\nimport torch\nfrom .meta_patch import meta_patched_function, meta_patched_module\n\n__all__ = ['is_element_in_list', 'extract_meta']\n\n\ndef is_element_in_list(elements: Union[List[Any], Any], list_: List[Any]):\n ... | [
{
"content": "from typing import Any, List, Union\n\nimport torch\n\nfrom ..proxy import ColoAttribute, ColoProxy\nfrom .meta_patch import meta_patched_function, meta_patched_module\n\n__all__ = ['is_element_in_list', 'extract_meta']\n\n\ndef is_element_in_list(elements: Union[List[Any], Any], list_: List[Any])... | diff --git a/colossalai/fx/tracer/_tracer_utils.py b/colossalai/fx/tracer/_tracer_utils.py
index 0ec49a90a133..e160497a7444 100644
--- a/colossalai/fx/tracer/_tracer_utils.py
+++ b/colossalai/fx/tracer/_tracer_utils.py
@@ -1,6 +1,8 @@
-from typing import List, Union, Any
-from ..proxy import ColoProxy, ColoAttribute
+from typing import Any, List, Union
+
import torch
+
+from ..proxy import ColoAttribute, ColoProxy
from .meta_patch import meta_patched_function, meta_patched_module
__all__ = ['is_element_in_list', 'extract_meta']
|
dmlc__dgl-2505 | jtnn example error
NOCUDA=1 python3 vaetrain_dgl.py
it shows NameError: name 'tensor' is not defined in dgl/examples/pytorch/jtnn/jtnn/nnutils.py", line 11, in cuda
return tensor
env:
dgl 0.5.3
torch 1.7.1
mac os
| [
{
"content": "import torch\nimport torch.nn as nn\nimport os\nimport dgl\n\n\ndef cuda(x):\n if torch.cuda.is_available() and not os.getenv('NOCUDA', None):\n return x.to(torch.device('cuda')) # works for both DGLGraph and tensor\n else:\n return tensor\n\n\nclass GRUUpdate(nn.Module):\n ... | [
{
"content": "import torch\nimport torch.nn as nn\nimport os\nimport dgl\n\n\ndef cuda(x):\n if torch.cuda.is_available() and not os.getenv('NOCUDA', None):\n return x.to(torch.device('cuda')) # works for both DGLGraph and tensor\n else:\n return x\n\n\nclass GRUUpdate(nn.Module):\n def... | diff --git a/examples/pytorch/jtnn/jtnn/nnutils.py b/examples/pytorch/jtnn/jtnn/nnutils.py
index 647edecb7fc8..8ef01ee25c4e 100644
--- a/examples/pytorch/jtnn/jtnn/nnutils.py
+++ b/examples/pytorch/jtnn/jtnn/nnutils.py
@@ -8,7 +8,7 @@ def cuda(x):
if torch.cuda.is_available() and not os.getenv('NOCUDA', None):
return x.to(torch.device('cuda')) # works for both DGLGraph and tensor
else:
- return tensor
+ return x
class GRUUpdate(nn.Module):
|
carpentries__amy-2126 | Community Roles: Date range validation
Currently, an end date earlier than start date is allowed.
| [
{
"content": "from collections import defaultdict\nfrom typing import Any, Optional\n\nfrom django import forms\nfrom django.core.exceptions import ObjectDoesNotExist, ValidationError\n\nfrom workshops.fields import HeavySelect2Widget, ModelSelect2Widget\nfrom workshops.forms import SELECT2_SIDEBAR, BootstrapHe... | [
{
"content": "from collections import defaultdict\nfrom typing import Any, Optional\n\nfrom django import forms\nfrom django.core.exceptions import ObjectDoesNotExist, ValidationError\n\nfrom workshops.fields import HeavySelect2Widget, ModelSelect2Widget\nfrom workshops.forms import SELECT2_SIDEBAR, BootstrapHe... | diff --git a/amy/communityroles/forms.py b/amy/communityroles/forms.py
index e210b3013..b4ac65552 100644
--- a/amy/communityroles/forms.py
+++ b/amy/communityroles/forms.py
@@ -127,3 +127,11 @@ def clean(self) -> dict[str, Any]:
raise ValidationError(errors)
return cleaned_data
+
+ def clean_end(self):
+ """Validate that end >= start"""
+ start = self.cleaned_data.get("start")
+ end = self.cleaned_data.get("end")
+ if start and end and end < start:
+ raise ValidationError("Must not be earlier than start date.")
+ return end
diff --git a/amy/communityroles/tests/test_community_role_form.py b/amy/communityroles/tests/test_community_role_form.py
index 63d294a45..fa503ef57 100644
--- a/amy/communityroles/tests/test_community_role_form.py
+++ b/amy/communityroles/tests/test_community_role_form.py
@@ -177,6 +177,48 @@ def test_membership_required(self):
["Membership is required with community role Test"],
)
+ def test_start_date_gt_end_date_is_invalid(self):
+ """Tests error raised if end < start"""
+ # Arrange
+ data = {
+ "start": date(2021, 11, 14),
+ "end": date(2021, 11, 13), # lt start
+ }
+
+ # Act
+ form = CommunityRoleForm(data)
+
+ # Assert
+ self.assertFalse(form.is_valid()) # errors expected
+ self.assertIn("end", form.errors.keys())
+ self.assertEqual(
+ form.errors["end"],
+ ["Must not be earlier than start date."],
+ )
+
+ def test_start_end_dates_valid(self):
+ """Tests valid start date <= end date"""
+ # Arrange
+ params = [
+ (date(2021, 11, 14), date(2021, 11, 14)),
+ (date(2021, 11, 14), date(2021, 11, 15)),
+ ]
+
+ for p1, p2 in params:
+ data = {
+ "start": p1,
+ "end": p2,
+ }
+
+ # Act
+ form = CommunityRoleForm(data)
+ form.is_valid()
+
+ # Assert
+ with self.subTest():
+ self.assertEqual(form.cleaned_data.get("end"), p2)
+ self.assertNotIn("end", form.errors.keys())
+
def test_additional_url_supported(self):
# Arrange
test_config = CommunityRoleConfig.objects.create(
|
paperless-ngx__paperless-ngx-2939 | [BUG] WebSocket connection failed when enable password auth in Redis
### Description
The browser gives an error saying "WebSocket connection to 'wss://domain.com/ws/status/' failed" when I enable the password authentication in `/etc/redis/redis.conf` as follows
```
user paperless on >password allcommands allkeys allchannels
```
And the corresponding paperless-ngx config is also updated
```
PAPERLESS_REDIS=redis://paperless:password@172.17.0.1:6379
```
Everything works fine when no password is applied to Redis.
### Steps to reproduce
1. Set up password in `/etc/redis/redis.conf` by adding a line `user paperless on >password allcommands allkeys allchannels`.
2. Update the paperless-ngx config as `PAPERLESS_REDIS=redis://paperless:password@172.17.0.1:6379`
3. Restart Redis
4. Restart Paperless-ngx
### Webserver logs
```bash
It seems that there are no useful logs related to this issue.
[2023-03-23 12:35:56,075] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2023-03-23 12:35:56,219] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2023-03-23 12:36:19,672] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
[2023-03-23 12:38:38,147] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2023-03-23 12:38:38,154] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2023-03-23 12:38:54,295] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
[2023-03-23 12:39:07,098] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2023-03-23 12:39:07,101] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2023-03-23 12:39:21,806] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
[2023-03-23 12:40:14,128] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2023-03-23 12:40:14,135] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2023-03-23 12:40:35,501] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
[2023-03-23 12:42:56,560] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2023-03-23 12:42:56,564] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2023-03-23 12:43:11,954] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
[2023-03-23 13:05:03,748] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2023-03-23 13:05:03,764] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2023-03-23 13:06:39,610] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
[2023-03-23 13:09:44,876] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2023-03-23 13:09:44,886] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2023-03-23 13:10:04,482] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
[2023-03-23 13:18:01,873] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2023-03-23 13:18:01,880] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2023-03-23 13:19:07,067] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
[2023-03-23 13:23:57,472] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2023-03-23 13:23:57,477] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2023-03-23 13:24:16,664] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
[2023-03-23 13:27:41,211] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2023-03-23 13:27:41,219] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2023-03-23 13:27:58,662] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
[2023-03-23 13:34:54,946] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2023-03-23 13:34:54,953] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2023-03-23 13:35:15,317] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
```
### Browser logs
```
WebSocket connection to 'wss://domain.com/ws/status/' failed:
connect @ main.js:3
ngOnInit @ main.js:3
MR @ main.js:3
FE @ main.js:3
fp @ main.js:3
xp @ main.js:3
Gp @ main.js:3
detectChanges @ main.js:3
tick @ main.js:3
_loadComponent @ main.js:3
bootstrap @ main.js:3
(anonymous) @ main.js:3
_moduleDoBootstrap @ main.js:3
(anonymous) @ main.js:3
invoke @ polyfills.js:1
onInvoke @ main.js:3
invoke @ polyfills.js:1
run @ polyfills.js:1
(anonymous) @ polyfills.js:1
invokeTask @ polyfills.js:1
onInvokeTask @ main.js:3
invokeTask @ polyfills.js:1
runTask @ polyfills.js:1
_ @ polyfills.js:1
invokeTask @ polyfills.js:1
Z @ polyfills.js:1
N @ polyfills.js:1
F @ polyfills.js:1
```
### Paperless-ngx version
1.13.0
### Host OS
CentOS Stream release 9
### Installation method
Docker - official image
### Browser
Chrome
### Configuration changes
I only use the ghcr.io/paperless-ngx/paperless-ngx image. The MySQL and Redis databases are from the host.
### Other
_No response_
| [
{
"content": "import datetime\nimport json\nimport math\nimport multiprocessing\nimport os\nimport re\nimport tempfile\nfrom os import PathLike\nfrom pathlib import Path\nfrom typing import Dict\nfrom typing import Final\nfrom typing import List\nfrom typing import Optional\nfrom typing import Set\nfrom typing ... | [
{
"content": "import datetime\nimport json\nimport math\nimport multiprocessing\nimport os\nimport re\nimport tempfile\nfrom os import PathLike\nfrom pathlib import Path\nfrom typing import Dict\nfrom typing import Final\nfrom typing import List\nfrom typing import Optional\nfrom typing import Set\nfrom typing ... | diff --git a/Pipfile b/Pipfile
index 8cf90a5dc5e..7058b8ff177 100644
--- a/Pipfile
+++ b/Pipfile
@@ -46,6 +46,7 @@ tika = "*"
# TODO: This will sadly also install daphne+dependencies,
# which an ASGI server we don't need. Adds about 15MB image size.
channels = "~=3.0"
+channels-redis = "*"
uvicorn = {extras = ["standard"], version = "*"}
concurrent-log-handler = "*"
"pdfminer.six" = "*"
@@ -62,9 +63,6 @@ bleach = "*"
#
# Pin this until piwheels is building 1.9 (see https://www.piwheels.org/project/scipy/)
scipy = "==1.8.1"
-# Locked version until https://github.com/django/channels_redis/issues/332
-# is resolved
-channels-redis = "==3.4.1"
[dev-packages]
coveralls = "*"
diff --git a/Pipfile.lock b/Pipfile.lock
index caf5b9a0886..b3bfc3ba810 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
{
"_meta": {
"hash": {
- "sha256": "8b3f3443de30aecc7c893d4a5a78123ba17e3dc10eed6042450a9c0cf6afcc3f"
+ "sha256": "01320f2ef2a561c37d17aaad61a7871b5a379dd1ac97fdaab586936b60dec92e"
},
"pipfile-spec": 6,
"requires": {},
@@ -19,13 +19,6 @@
]
},
"default": {
- "aioredis": {
- "hashes": [
- "sha256:15f8af30b044c771aee6787e5ec24694c048184c7b9e54c3b60c750a4b93273a",
- "sha256:b61808d7e97b7cd5a92ed574937a079c9387fdadd22bfbfa7ad2fd319ecc26e3"
- ],
- "version": "==1.3.1"
- },
"amqp": {
"hashes": [
"sha256:2c1b13fecc0893e946c65cbd5f36427861cffa4ea2201d8f6fca22e2a373b5e2",
@@ -55,7 +48,7 @@
"sha256:2163e1640ddb52b7a8c80d0a67a08587e5d245cc9c553a74a847056bc2976b15",
"sha256:8ca1e4fcf50d07413d66d1a5e416e42cfdf5851c981d679a09851a6853383b3c"
],
- "markers": "python_version >= '3.6'",
+ "markers": "python_version < '3.11'",
"version": "==4.0.2"
},
"attrs": {
@@ -302,11 +295,11 @@
},
"channels-redis": {
"hashes": [
- "sha256:78e4a2f2b2a744fe5a87848ec36b5ee49f522c6808cefe6c583663d0d531faa8",
- "sha256:ba7e2ad170f273c372812dd32aaac102d68d4e508172abb1cfda3160b7333890"
+ "sha256:122414f29f525f7b9e0c9d59cdcfc4dc1b0eecba16fbb6a1c23f1d9b58f49dcb",
+ "sha256:81b59d68f53313e1aa891f23591841b684abb936b42e4d1a966d9e4dc63a95ec"
],
"index": "pypi",
- "version": "==3.4.1"
+ "version": "==4.0.0"
},
"charset-normalizer": {
"hashes": [
@@ -481,11 +474,11 @@
},
"dateparser": {
"hashes": [
- "sha256:fbed8b738a24c9cd7f47c4f2089527926566fe539e1a06125eddba75917b1eef",
- "sha256:ff047d9cffad4d3113ead8ec0faf8a7fc43bab7d853ac8715e071312b53c465a"
+ "sha256:070b29b5bbf4b1ec2cd51c96ea040dc68a614de703910a91ad1abba18f9f379f",
+ "sha256:86b8b7517efcc558f085a142cdb7620f0921543fcabdb538c8a4c4001d8178e3"
],
"index": "pypi",
- "version": "==1.1.7"
+ "version": "==1.1.8"
},
"deprecation": {
"hashes": [
@@ -576,11 +569,11 @@
},
"filelock": {
"hashes": [
- "sha256:4427cdda14a1c68e264845142842d6de2d0fa2c15ba31571a3d9c9a1ec9d191c",
- "sha256:e393782f76abea324dee598d2ea145b857a20df0e0ee4f80fcf35e72a341d2c7"
+ "sha256:75997740323c5f12e18f10b494bc11c03e42843129f980f17c04352cc7b09d40",
+ "sha256:eb8f0f2d37ed68223ea63e3bddf2fac99667e4362c88b3f762e434d160190d18"
],
"index": "pypi",
- "version": "==3.9.1"
+ "version": "==3.10.2"
},
"flower": {
"hashes": [
@@ -1053,11 +1046,11 @@
},
"ocrmypdf": {
"hashes": [
- "sha256:8fab75052bf77c3488acd9c3054423d9f1f7650e302960a1fa2e991f36c2a66a",
- "sha256:db03cdd1a5d277fa038b0420ba05fcf7b1f92729ba85431344844ebf01035160"
+ "sha256:779b6f77ece5836b4ac703ba02a4bb0ccb758dbb9b4dad1feab3fccd4dba33cf",
+ "sha256:c731bd3b6bfd67dc495edc97946f159ba99631854bf7671c2d35c36f30b3ffa8"
],
"index": "pypi",
- "version": "==14.0.3"
+ "version": "==14.0.4"
},
"packaging": {
"hashes": [
@@ -1515,105 +1508,77 @@
"hiredis"
],
"hashes": [
- "sha256:1eec3741cda408d3a5f84b78d089c8b8d895f21b3b050988351e925faf202864",
- "sha256:5deb072d26e67d2be1712603bfb7947ec3431fb0eec9c578994052e33035af6d"
+ "sha256:56732e156fe31801c4f43396bd3ca0c2a7f6f83d7936798531b9848d103381aa",
+ "sha256:7df17a0a2b72a4c8895b462dd07616c51b1dcb48fdd7ecb7b6f4bf39ecb2e94e"
],
"index": "pypi",
- "version": "==4.5.1"
+ "version": "==4.5.3"
},
"regex": {
"hashes": [
- "sha256:052b670fafbe30966bbe5d025e90b2a491f85dfe5b2583a163b5e60a85a321ad",
- "sha256:0653d012b3bf45f194e5e6a41df9258811ac8fc395579fa82958a8b76286bea4",
- "sha256:0a069c8483466806ab94ea9068c34b200b8bfc66b6762f45a831c4baaa9e8cdd",
- "sha256:0cf0da36a212978be2c2e2e2d04bdff46f850108fccc1851332bcae51c8907cc",
- "sha256:131d4be09bea7ce2577f9623e415cab287a3c8e0624f778c1d955ec7c281bd4d",
- "sha256:144486e029793a733e43b2e37df16a16df4ceb62102636ff3db6033994711066",
- "sha256:1ddf14031a3882f684b8642cb74eea3af93a2be68893901b2b387c5fd92a03ec",
- "sha256:1eba476b1b242620c266edf6325b443a2e22b633217a9835a52d8da2b5c051f9",
- "sha256:20f61c9944f0be2dc2b75689ba409938c14876c19d02f7585af4460b6a21403e",
- "sha256:22960019a842777a9fa5134c2364efaed5fbf9610ddc5c904bd3a400973b0eb8",
- "sha256:22e7ebc231d28393dfdc19b185d97e14a0f178bedd78e85aad660e93b646604e",
- "sha256:23cbb932cc53a86ebde0fb72e7e645f9a5eec1a5af7aa9ce333e46286caef783",
- "sha256:29c04741b9ae13d1e94cf93fca257730b97ce6ea64cfe1eba11cf9ac4e85afb6",
- "sha256:2bde29cc44fa81c0a0c8686992c3080b37c488df167a371500b2a43ce9f026d1",
- "sha256:2cdc55ca07b4e70dda898d2ab7150ecf17c990076d3acd7a5f3b25cb23a69f1c",
- "sha256:370f6e97d02bf2dd20d7468ce4f38e173a124e769762d00beadec3bc2f4b3bc4",
- "sha256:395161bbdbd04a8333b9ff9763a05e9ceb4fe210e3c7690f5e68cedd3d65d8e1",
- "sha256:44136355e2f5e06bf6b23d337a75386371ba742ffa771440b85bed367c1318d1",
- "sha256:44a6c2f6374e0033873e9ed577a54a3602b4f609867794c1a3ebba65e4c93ee7",
- "sha256:4919899577ba37f505aaebdf6e7dc812d55e8f097331312db7f1aab18767cce8",
- "sha256:4b4b1fe58cd102d75ef0552cf17242705ce0759f9695334a56644ad2d83903fe",
- "sha256:4bdd56ee719a8f751cf5a593476a441c4e56c9b64dc1f0f30902858c4ef8771d",
- "sha256:4bf41b8b0a80708f7e0384519795e80dcb44d7199a35d52c15cc674d10b3081b",
- "sha256:4cac3405d8dda8bc6ed499557625585544dd5cbf32072dcc72b5a176cb1271c8",
- "sha256:4fe7fda2fe7c8890d454f2cbc91d6c01baf206fbc96d89a80241a02985118c0c",
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+ "sha256:f1977c1fe28173f2349d42c59f80f10a97ce34f2bedb7b7f55e2e8a8de9b7dfb",
+ "sha256:f2bc8a9076ea7add860d57dbee0554a212962ecf2a900344f2fc7c56a02463b0",
+ "sha256:f311ca33fcb9f8fb060c1fa76238d8d029f33b71a2021bafa5d423cc25965b54",
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+ "sha256:fa41a427d4f03ec6d6da2fd8a230f4f388f336cd7ca46b46c4d2a1bca3ead85a",
+ "sha256:fd47362e03acc780aad5a5bc4624d495594261b55a1f79a5b775b6be865a5911"
],
- "markers": "python_version >= '3.6'",
- "version": "==2022.10.31"
+ "markers": "python_version >= '3.8'",
+ "version": "==2023.3.22"
},
"reportlab": {
"hashes": [
@@ -1925,11 +1890,11 @@
},
"tzlocal": {
"hashes": [
- "sha256:89885494684c929d9191c57aa27502afc87a579be5cdd3225c77c463ea043745",
- "sha256:ee5842fa3a795f023514ac2d801c4a81d1743bbe642e3940143326b3a00addd7"
+ "sha256:3f21d09e1b2aa9f2dacca12da240ca37de3ba5237a93addfd6d593afe9073355",
+ "sha256:b44c4388f3d34f25862cfbb387578a4d70fec417649da694a132f628a23367e2"
],
- "markers": "python_version >= '3.6'",
- "version": "==4.2"
+ "markers": "python_version >= '3.7'",
+ "version": "==4.3"
},
"urllib3": {
"hashes": [
@@ -1944,11 +1909,11 @@
"standard"
],
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],
"index": "pypi",
- "version": "==0.21.0"
+ "version": "==0.21.1"
},
"uvloop": {
"hashes": [
@@ -2165,45 +2130,39 @@
},
"zope.interface": {
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- "sha256:fb68d212efd057596dee9e6582daded9f8ef776538afdf5feceb3059df2d2e7b"
+ "sha256:042f2381118b093714081fd82c98e3b189b68db38ee7d35b63c327c470ef8373",
+ "sha256:0ec9653825f837fbddc4e4b603d90269b501486c11800d7c761eee7ce46d1bbb",
+ "sha256:12175ca6b4db7621aedd7c30aa7cfa0a2d65ea3a0105393e05482d7a2d367446",
+ "sha256:1592f68ae11e557b9ff2bc96ac8fc30b187e77c45a3c9cd876e3368c53dc5ba8",
+ "sha256:23ac41d52fd15dd8be77e3257bc51bbb82469cf7f5e9a30b75e903e21439d16c",
+ "sha256:424d23b97fa1542d7be882eae0c0fc3d6827784105264a8169a26ce16db260d8",
+ "sha256:4407b1435572e3e1610797c9203ad2753666c62883b921318c5403fb7139dec2",
+ "sha256:48f4d38cf4b462e75fac78b6f11ad47b06b1c568eb59896db5b6ec1094eb467f",
+ "sha256:4c3d7dfd897a588ec27e391edbe3dd320a03684457470415870254e714126b1f",
+ "sha256:5171eb073474a5038321409a630904fd61f12dd1856dd7e9d19cd6fe092cbbc5",
+ "sha256:5a158846d0fca0a908c1afb281ddba88744d403f2550dc34405c3691769cdd85",
+ "sha256:6ee934f023f875ec2cfd2b05a937bd817efcc6c4c3f55c5778cbf78e58362ddc",
+ "sha256:790c1d9d8f9c92819c31ea660cd43c3d5451df1df61e2e814a6f99cebb292788",
+ "sha256:809fe3bf1a91393abc7e92d607976bbb8586512913a79f2bf7d7ec15bd8ea518",
+ "sha256:87b690bbee9876163210fd3f500ee59f5803e4a6607d1b1238833b8885ebd410",
+ "sha256:89086c9d3490a0f265a3c4b794037a84541ff5ffa28bb9c24cc9f66566968464",
+ "sha256:99856d6c98a326abbcc2363827e16bd6044f70f2ef42f453c0bd5440c4ce24e5",
+ "sha256:aab584725afd10c710b8f1e6e208dbee2d0ad009f57d674cb9d1b3964037275d",
+ "sha256:af169ba897692e9cd984a81cb0f02e46dacdc07d6cf9fd5c91e81f8efaf93d52",
+ "sha256:b39b8711578dcfd45fc0140993403b8a81e879ec25d53189f3faa1f006087dca",
+ "sha256:b3f543ae9d3408549a9900720f18c0194ac0fe810cecda2a584fd4dca2eb3bb8",
+ "sha256:d0583b75f2e70ec93f100931660328965bb9ff65ae54695fb3fa0a1255daa6f2",
+ "sha256:dfbbbf0809a3606046a41f8561c3eada9db811be94138f42d9135a5c47e75f6f",
+ "sha256:e538f2d4a6ffb6edfb303ce70ae7e88629ac6e5581870e66c306d9ad7b564a58",
+ "sha256:eba51599370c87088d8882ab74f637de0c4f04a6d08a312dce49368ba9ed5c2a",
+ "sha256:ee4b43f35f5dc15e1fec55ccb53c130adb1d11e8ad8263d68b1284b66a04190d",
+ "sha256:f2363e5fd81afb650085c6686f2ee3706975c54f331b426800b53531191fdf28",
+ "sha256:f299c020c6679cb389814a3b81200fe55d428012c5e76da7e722491f5d205990",
+ "sha256:f72f23bab1848edb7472309e9898603141644faec9fd57a823ea6b4d1c4c8995",
+ "sha256:fa90bac61c9dc3e1a563e5babb3fd2c0c1c80567e815442ddbe561eadc803b30"
],
- "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
- "version": "==5.5.2"
+ "markers": "python_version >= '3.7'",
+ "version": "==6.0"
},
"zstandard": {
"hashes": [
@@ -2519,19 +2478,19 @@
},
"faker": {
"hashes": [
- "sha256:51f37ff9df710159d6d736d0ba1c75e063430a8c806b91334d7794305b5a6114",
- "sha256:5aaa16fa9cfde7d117eef70b6b293a705021e57158f3fa6b44ed1b70202d2065"
+ "sha256:2deeee8fed3d1b8ae5f87d172d4569ddc859aab8693f7cd68eddc5d20400563a",
+ "sha256:e7c058e1f360f245f265625b32d3189d7229398ad80a8b6bac459891745de052"
],
"markers": "python_version >= '3.7'",
- "version": "==17.6.0"
+ "version": "==18.3.0"
},
"filelock": {
"hashes": [
- "sha256:4427cdda14a1c68e264845142842d6de2d0fa2c15ba31571a3d9c9a1ec9d191c",
- "sha256:e393782f76abea324dee598d2ea145b857a20df0e0ee4f80fcf35e72a341d2c7"
+ "sha256:75997740323c5f12e18f10b494bc11c03e42843129f980f17c04352cc7b09d40",
+ "sha256:eb8f0f2d37ed68223ea63e3bddf2fac99667e4362c88b3f762e434d160190d18"
],
"index": "pypi",
- "version": "==3.9.1"
+ "version": "==3.10.2"
},
"ghp-import": {
"hashes": [
@@ -2542,11 +2501,11 @@
},
"identify": {
"hashes": [
- "sha256:5dfef8a745ca4f2c95f27e9db74cb4c8b6d9916383988e8791f3595868f78a33",
- "sha256:c8b288552bc5f05a08aff09af2f58e6976bf8ac87beb38498a0e3d98ba64eb18"
+ "sha256:69edcaffa8e91ae0f77d397af60f148b6b45a8044b2cc6d99cafa5b04793ff00",
+ "sha256:7671a05ef9cfaf8ff63b15d45a91a1147a03aaccb2976d4e9bd047cbbc508471"
],
"markers": "python_version >= '3.7'",
- "version": "==2.5.20"
+ "version": "==2.5.21"
},
"idna": {
"hashes": [
@@ -2566,11 +2525,11 @@
},
"importlib-metadata": {
"hashes": [
- "sha256:7efb448ec9a5e313a57655d35aa54cd3e01b7e1fbcf72dce1bf06119420f5bad",
- "sha256:e354bedeb60efa6affdcc8ae121b73544a7aa74156d047311948f6d711cd378d"
+ "sha256:43ce9281e097583d758c2c708c4376371261a02c34682491a8e98352365aad20",
+ "sha256:ff80f3b5394912eb1b108fcfd444dc78b7f1f3e16b16188054bd01cb9cb86f09"
],
"markers": "python_version < '3.10'",
- "version": "==6.0.0"
+ "version": "==6.1.0"
},
"iniconfig": {
"hashes": [
@@ -2744,11 +2703,11 @@
},
"pathspec": {
"hashes": [
- "sha256:3a66eb970cbac598f9e5ccb5b2cf58930cd8e3ed86d393d541eaf2d8b1705229",
- "sha256:64d338d4e0914e91c1792321e6907b5a593f1ab1851de7fc269557a21b30ebbc"
+ "sha256:2798de800fa92780e33acca925945e9a19a133b715067cf165b8866c15a31687",
+ "sha256:d8af70af76652554bd134c22b3e8a1cc46ed7d91edcdd721ef1a0c51a84a5293"
],
"markers": "python_version >= '3.7'",
- "version": "==0.11.0"
+ "version": "==0.11.1"
},
"pillow": {
"hashes": [
@@ -2851,11 +2810,11 @@
},
"pre-commit": {
"hashes": [
- "sha256:b80254e60668e1dd1f5c03a1c9e0413941d61f568a57d745add265945f65bfe8",
- "sha256:d63e6537f9252d99f65755ae5b79c989b462d511ebbc481b561db6a297e1e865"
+ "sha256:818f0d998059934d0f81bb3667e3ccdc32da6ed7ccaac33e43dc231561ddaaa9",
+ "sha256:f712d3688102e13c8e66b7d7dbd8934a6dda157e58635d89f7d6fecdca39ce8a"
],
"index": "pypi",
- "version": "==3.1.1"
+ "version": "==3.2.0"
},
"pygments": {
"hashes": [
@@ -3015,97 +2974,69 @@
},
"regex": {
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+ "sha256:4ad467524cb6879ce42107cf02a49cdb4a06f07fe0e5f1160d7db865a8d25d4b",
+ "sha256:4c9c3db90acd17e4231344a23616f33fd79837809584ce30e2450ca312fa47aa",
+ "sha256:533ba64d67d882286557106a1c5f12b4c2825f11b47a7c209a8c22922ca882be",
+ "sha256:548257463696daf919d2fdfc53ee4b98e29e3ffc5afddd713d83aa849d1fa178",
+ "sha256:55f907c4d18a5a40da0ceb339a0beda77c9df47c934adad987793632fb4318c3",
+ "sha256:5826e7fb443acb49f64f9648a2852efc8d9af2f4c67f6c3dca69dccd9e8e1d15",
+ "sha256:59a15c2803c20702d7f2077807d9a2b7d9a168034b87fd3f0d8361de60019a1e",
+ "sha256:59b3aab231c27cd754d6452c43b12498d34e7ab87d69a502bd0220f4b1c090c4",
+ "sha256:5da83c964aecb6c3f2a6c9a03f3d0fa579e1ad208e2c264ba826cecd19da11fa",
+ "sha256:60b545806a433cc752b9fa936f1c0a63bf96a3872965b958b35bd0d5d788d411",
+ "sha256:60fcef5c3144d861b623456d87ca7fff7af59a4a918e1364cdd0687b48285285",
+ "sha256:617d101b95151d827d5366e9c4225a68c64d56065e41ab9c7ef51bb87f347a8a",
+ "sha256:68e9add923bda8357e6fe65a568766feae369063cb7210297067675cce65272f",
+ "sha256:7798b3d662f70cea425637c54da30ef1894d426cab24ee7ffaaccb24a8b17bb8",
+ "sha256:80a288b21b17e39fb3630cf1d14fd704499bb11d9c8fc110662a0c57758d3d3e",
+ "sha256:81291006a934052161eae8340e7731ea6b8595b0c27dd4927c4e8a489e1760e2",
+ "sha256:8527ea0978ed6dc58ccb3935bd2883537b455c97ec44b5d8084677dfa817f96b",
+ "sha256:87016850c13082747bd120558e6750746177bd492b103b2fca761c8a1c43fba9",
+ "sha256:88552925fd22320600c59ee80342d6eb06bfa9503c3a402d7327983f5fa999d9",
+ "sha256:8d7477ebaf5d3621c763702e1ec0daeede8863fb22459c5e26ddfd17e9b1999c",
+ "sha256:97326d62255203c6026896d4b1ad6b5a0141ba097cae00ed3a508fe454e96baf",
+ "sha256:a4c7b8c5a3a186b49415af3be18e4b8f93b33d6853216c0a1d7401736b703bce",
+ "sha256:aff7c778d9229d66f716ad98a701fa91cf97935ae4a32a145ae9e61619906aaa",
+ "sha256:b280cb303fed94199f0b976595af71ebdcd388fb5e377a8198790f1016a23476",
+ "sha256:b59233cb8df6b60fff5f3056f6f342a8f5f04107a11936bf49ebff87dd4ace34",
+ "sha256:bdab2c90665b88faf5cc5e11bf835d548f4b8d8060c89fc70782b6020850aa1c",
+ "sha256:c00c357a4914f58398503c7f716cf1646b1e36b8176efa35255f5ebfacedfa46",
+ "sha256:c95a977cfdccb8ddef95ddd77cf586fe9dc327c7c93cf712983cece70cdaa1be",
+ "sha256:cdd3d2df486c9a8c6d08f78bdfa8ea7cf6191e037fde38c2cf6f5f0559e9d353",
+ "sha256:d15a0cc48f7a3055e89df1bd6623a907c407d1f58f67ff47064e598d4a550de4",
+ "sha256:d40cecf4bcb2cb37c59e3c79e5bbc45d47e3f3e07edf24e35fc5775db2570058",
+ "sha256:d4d3571c8eb21f0fbe9f0b21b49092c24d442f9a295f079949df3551b2886f29",
+ "sha256:d94a0d25e517c76c9ce9e2e2635d9d1a644b894f466a66a10061f4e599cdc019",
+ "sha256:dcc5b0d6a94637c071a427dc4469efd0ae4fda8ff384790bc8b5baaf9308dc3e",
+ "sha256:e00b046000b313ffaa2f6e8d7290b33b08d2005150eff4c8cf3ad74d011888d1",
+ "sha256:e1b56dac5e86ab52e0443d63b02796357202a8f8c5966b69f8d4c03a94778e98",
+ "sha256:e30d9a6fd7a7a6a4da6f80d167ce8eda4a993ff24282cbc73f34186c46a498db",
+ "sha256:f1977c1fe28173f2349d42c59f80f10a97ce34f2bedb7b7f55e2e8a8de9b7dfb",
+ "sha256:f2bc8a9076ea7add860d57dbee0554a212962ecf2a900344f2fc7c56a02463b0",
+ "sha256:f311ca33fcb9f8fb060c1fa76238d8d029f33b71a2021bafa5d423cc25965b54",
+ "sha256:f579a202b90c1110d0894a86b32a89bf550fdb34bdd3f9f550115706be462e19",
+ "sha256:fa41a427d4f03ec6d6da2fd8a230f4f388f336cd7ca46b46c4d2a1bca3ead85a",
+ "sha256:fd47362e03acc780aad5a5bc4624d495594261b55a1f79a5b775b6be865a5911"
],
- "markers": "python_version >= '3.6'",
- "version": "==2022.10.31"
+ "markers": "python_version >= '3.8'",
+ "version": "==2023.3.22"
},
"requests": {
"hashes": [
@@ -3490,30 +3421,30 @@
"compatible-mypy"
],
"hashes": [
- "sha256:0bbf9eb172c5b06eccff2d704c7c3906e4a2c6146df8c32ee9f3a51e29265581",
- "sha256:25010658acac0ce4a69211b55dd719fd16dbfe54fcfe5c878d0c8db07bdd5482"
+ "sha256:1bd96207576cd220221a0e615f0259f13d453d515a80f576c1246e0fb547f561",
+ "sha256:c95f948e2bfc565f3147e969ff361ef033841a0b8a51cac974a6cc6d0486732c"
],
"index": "pypi",
- "version": "==1.15.0"
+ "version": "==1.16.0"
},
"django-stubs-ext": {
"hashes": [
- "sha256:4fd8cdbc68d1a421f21bb7e0d9e76d50f6a4b504d350ba786405daf536e90c21",
- "sha256:d729fbc7fe8970a7e26b35956c35b48502516f011d523c0577bdfb02ed956284"
+ "sha256:9a9ba9e2808737949de96a0fce8b054f12d38e461011d77ebc074ffe8c43dfcb",
+ "sha256:a454d349d19c26d6c50c4c6dbc1e8af4a9cda4ce1dc4104e3dd4c0330510cc56"
],
"markers": "python_version >= '3.7'",
- "version": "==0.7.0"
+ "version": "==0.8.0"
},
"djangorestframework-stubs": {
"extras": [
"compatible-mypy"
],
"hashes": [
- "sha256:89f6c2add193cb5ab61b9e47187b33a93cc099376a8df5e4d6c3fc8ecb992d3b",
- "sha256:9475e1374b057ffbdcaaa84a060fe5f01476d8b9014d82a83b4153f57fbcbc1f"
+ "sha256:433edd7f10786914138b300b9be5aba1ebc80c471b5156934664afd7e9df9fd6",
+ "sha256:69e8a1ea7eb815cbe35155c27eee72522d7c8666d3cbdacb9997ab88c7b4202c"
],
"index": "pypi",
- "version": "==1.9.1"
+ "version": "==1.10.0"
},
"idna": {
"hashes": [
@@ -3525,35 +3456,35 @@
},
"mypy": {
"hashes": [
- "sha256:0af4f0e20706aadf4e6f8f8dc5ab739089146b83fd53cb4a7e0e850ef3de0bb6",
- "sha256:15b5a824b58c7c822c51bc66308e759243c32631896743f030daf449fe3677f3",
- "sha256:17455cda53eeee0a4adb6371a21dd3dbf465897de82843751cf822605d152c8c",
- "sha256:2013226d17f20468f34feddd6aae4635a55f79626549099354ce641bc7d40262",
- "sha256:24189f23dc66f83b839bd1cce2dfc356020dfc9a8bae03978477b15be61b062e",
- "sha256:27a0f74a298769d9fdc8498fcb4f2beb86f0564bcdb1a37b58cbbe78e55cf8c0",
- "sha256:28cea5a6392bb43d266782983b5a4216c25544cd7d80be681a155ddcdafd152d",
- "sha256:448de661536d270ce04f2d7dddaa49b2fdba6e3bd8a83212164d4174ff43aa65",
- "sha256:48525aec92b47baed9b3380371ab8ab6e63a5aab317347dfe9e55e02aaad22e8",
- "sha256:5bc8d6bd3b274dd3846597855d96d38d947aedba18776aa998a8d46fabdaed76",
- "sha256:5deb252fd42a77add936b463033a59b8e48eb2eaec2976d76b6878d031933fe4",
- "sha256:5f546ac34093c6ce33f6278f7c88f0f147a4849386d3bf3ae193702f4fe31407",
- "sha256:5fdd63e4f50e3538617887e9aee91855368d9fc1dea30da743837b0df7373bc4",
- "sha256:65b122a993d9c81ea0bfde7689b3365318a88bde952e4dfa1b3a8b4ac05d168b",
- "sha256:71a808334d3f41ef011faa5a5cd8153606df5fc0b56de5b2e89566c8093a0c9a",
- "sha256:920169f0184215eef19294fa86ea49ffd4635dedfdea2b57e45cb4ee85d5ccaf",
- "sha256:93a85495fb13dc484251b4c1fd7a5ac370cd0d812bbfc3b39c1bafefe95275d5",
- "sha256:a2948c40a7dd46c1c33765718936669dc1f628f134013b02ff5ac6c7ef6942bf",
- "sha256:c6c2ccb7af7154673c591189c3687b013122c5a891bb5651eca3db8e6c6c55bd",
- "sha256:c96b8a0c019fe29040d520d9257d8c8f122a7343a8307bf8d6d4a43f5c5bfcc8",
- "sha256:d42a98e76070a365a1d1c220fcac8aa4ada12ae0db679cb4d910fabefc88b994",
- "sha256:dbeb24514c4acbc78d205f85dd0e800f34062efcc1f4a4857c57e4b4b8712bff",
- "sha256:e60d0b09f62ae97a94605c3f73fd952395286cf3e3b9e7b97f60b01ddfbbda88",
- "sha256:e64f48c6176e243ad015e995de05af7f22bbe370dbb5b32bd6988438ec873919",
- "sha256:e831662208055b006eef68392a768ff83596035ffd6d846786578ba1714ba8f6",
- "sha256:eda5c8b9949ed411ff752b9a01adda31afe7eae1e53e946dbdf9db23865e66c4"
- ],
- "index": "pypi",
- "version": "==1.0.1"
+ "sha256:0a28a76785bf57655a8ea5eb0540a15b0e781c807b5aa798bd463779988fa1d5",
+ "sha256:19ba15f9627a5723e522d007fe708007bae52b93faab00f95d72f03e1afa9598",
+ "sha256:21b437be1c02712a605591e1ed1d858aba681757a1e55fe678a15c2244cd68a5",
+ "sha256:26cdd6a22b9b40b2fd71881a8a4f34b4d7914c679f154f43385ca878a8297389",
+ "sha256:2888ce4fe5aae5a673386fa232473014056967f3904f5abfcf6367b5af1f612a",
+ "sha256:2b0c373d071593deefbcdd87ec8db91ea13bd8f1328d44947e88beae21e8d5e9",
+ "sha256:315ac73cc1cce4771c27d426b7ea558fb4e2836f89cb0296cbe056894e3a1f78",
+ "sha256:39c7119335be05630611ee798cc982623b9e8f0cff04a0b48dfc26100e0b97af",
+ "sha256:4b398d8b1f4fba0e3c6463e02f8ad3346f71956b92287af22c9b12c3ec965a9f",
+ "sha256:4e4e8b362cdf99ba00c2b218036002bdcdf1e0de085cdb296a49df03fb31dfc4",
+ "sha256:59bbd71e5c58eed2e992ce6523180e03c221dcd92b52f0e792f291d67b15a71c",
+ "sha256:5b5f81b40d94c785f288948c16e1f2da37203c6006546c5d947aab6f90aefef2",
+ "sha256:5cb14ff9919b7df3538590fc4d4c49a0f84392237cbf5f7a816b4161c061829e",
+ "sha256:61bf08362e93b6b12fad3eab68c4ea903a077b87c90ac06c11e3d7a09b56b9c1",
+ "sha256:64cc3afb3e9e71a79d06e3ed24bb508a6d66f782aff7e56f628bf35ba2e0ba51",
+ "sha256:69b35d1dcb5707382810765ed34da9db47e7f95b3528334a3c999b0c90fe523f",
+ "sha256:9401e33814cec6aec8c03a9548e9385e0e228fc1b8b0a37b9ea21038e64cdd8a",
+ "sha256:a380c041db500e1410bb5b16b3c1c35e61e773a5c3517926b81dfdab7582be54",
+ "sha256:ae9ceae0f5b9059f33dbc62dea087e942c0ccab4b7a003719cb70f9b8abfa32f",
+ "sha256:b7c7b708fe9a871a96626d61912e3f4ddd365bf7f39128362bc50cbd74a634d5",
+ "sha256:c1c10fa12df1232c936830839e2e935d090fc9ee315744ac33b8a32216b93707",
+ "sha256:ce61663faf7a8e5ec6f456857bfbcec2901fbdb3ad958b778403f63b9e606a1b",
+ "sha256:d64c28e03ce40d5303450f547e07418c64c241669ab20610f273c9e6290b4b0b",
+ "sha256:d809f88734f44a0d44959d795b1e6f64b2bbe0ea4d9cc4776aa588bb4229fc1c",
+ "sha256:dbb19c9f662e41e474e0cff502b7064a7edc6764f5262b6cd91d698163196799",
+ "sha256:ef6a01e563ec6a4940784c574d33f6ac1943864634517984471642908b30b6f7"
+ ],
+ "index": "pypi",
+ "version": "==1.1.1"
},
"mypy-extensions": {
"hashes": [
@@ -3704,26 +3635,26 @@
},
"types-redis": {
"hashes": [
- "sha256:43d92b4d6315a45bb0e9a790683ba4448ada88cd1233f3f9886fa6f783f53956",
- "sha256:f516254bd593023110a38b77e80d5a76a7f033f1d94c53bee09a7d5d0433f34d"
+ "sha256:7c1d5fdb0a2d5fd92eac37ce382fdb47d99a69889e7d6c2bc4479148ac646c73",
+ "sha256:f23415e448ca25ec5028c24fdf3717a13f0c905eb1933733e8a8a7d4952f6908"
],
"index": "pypi",
- "version": "==4.5.1.5"
+ "version": "==4.5.3.0"
},
"types-requests": {
"hashes": [
- "sha256:a05e4c7bc967518fba5789c341ea8b0c942776ee474c7873129a61161978e586",
- "sha256:fc8eaa09cc014699c6b63c60c2e3add0c8b09a410c818b5ac6e65f92a26dde09"
+ "sha256:9d4002056df7ebc4ec1f28fd701fba82c5c22549c4477116cb2656aa30ace6db",
+ "sha256:a86921028335fdcc3aaf676c9d3463f867db6af2303fc65aa309b13ae1e6dd53"
],
- "version": "==2.28.11.15"
+ "version": "==2.28.11.16"
},
"types-setuptools": {
"hashes": [
- "sha256:70b5e6a379e9fccf6579871a93ca3301a46252e3ae66957ec64281a2b6a812d9",
- "sha256:d669a80ee8e37eb1697dc31a23d41ea2c48a635464e2c7e6370dda811459b466"
+ "sha256:3a708e66c7bdc620e4d0439f344c750c57a4340c895a4c3ed2d0fc4ae8eb9962",
+ "sha256:dae5a4a659dbb6dba57773440f6e2dbdd8ef282dc136a174a8a59bd33d949945"
],
"index": "pypi",
- "version": "==67.6.0.0"
+ "version": "==67.6.0.5"
},
"types-tqdm": {
"hashes": [
diff --git a/src/paperless/settings.py b/src/paperless/settings.py
index c809f0a7a2d..e7f53d8ce1e 100644
--- a/src/paperless/settings.py
+++ b/src/paperless/settings.py
@@ -358,7 +358,7 @@ def _parse_beat_schedule() -> Dict:
CHANNEL_LAYERS = {
"default": {
- "BACKEND": "channels_redis.core.RedisChannelLayer",
+ "BACKEND": "channels_redis.pubsub.RedisPubSubChannelLayer",
"CONFIG": {
"hosts": [_CHANNELS_REDIS_URL],
"capacity": 2000, # default 100
|
python-pillow__Pillow-3042 | JPEG 2K, PyImaging_Jpeg2KDecoderNew function takes at most 6 arguments (7 given)
### What did you do?
Try to upload a JPEG 2000
### What did you expect to happen?
Validate width and height
### What actually happened?
Exception
### What versions of Pillow and Python are you using?
Pillow 5.0.0
Python 2.7.12
Image: http://ghpublic.s3-us-west-1.amazonaws.com/relax.jp2
> TypeError: function takes at most 6 arguments (7 given)
> File "django/core/handlers/exception.py", line 41, in inner
> response = get_response(request)
> File "django/core/handlers/base.py", line 249, in _legacy_get_response
> response = self._get_response(request)
> File "django/core/handlers/base.py", line 187, in _get_response
> response = self.process_exception_by_middleware(e, request)
> File "django/core/handlers/base.py", line 185, in _get_response
> response = wrapped_callback(request, *callback_args, **callback_kwargs)
> File "newrelic/hooks/framework_django.py", line 527, in wrapper
> return wrapped(*args, **kwargs)
> File "django/contrib/auth/decorators.py", line 23, in _wrapped_view
> return view_func(request, *args, **kwargs)
> File "gh_admin/views.py", line 917, in ajax_image_uploader
> if None in [image.image.height, image.image.width]:
> File "django/core/files/images.py", line 23, in height
> return self._get_image_dimensions()[1]
> File "django/core/files/images.py", line 29, in _get_image_dimensions
> self._dimensions_cache = get_image_dimensions(self, close=close)
> File "django/core/files/images.py", line 59, in get_image_dimensions
> p.feed(data)
> File "PIL/ImageFile.py", line 411, in feed
> self.decoder = Image._getdecoder(im.mode, d, a, im.decoderconfig)
> ```
> a | ['jp2', 0, 0, -1, 1024, <_io.BytesIO object at 0x7f67f5a86710>]
> d | 'jpeg2k'
> data | '\x00\x00\x00\x0cjP \r\n\x87\n\x00\x00\x00\x14ftypjp2 \x00\x00\x00\x00jp2 \x00\x00\x01Yjp2h\x00\x00\x00\x16ihdr\x00\x00\x01,\x00\x00\x01\x90\x00\x03\x07\x07\x01\x00\x00\x00\x01!colr\x02\x00\x00\x00\x00\x01\x16\x00\x00\x00\x00\x02 \x00\x00scnrRGB XYZ \x07\xd1\x00\x01\x00\x01\x00\x00\x00\x00\x00\x00acsp\x00\x00\x00\x00\x00\xc0\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x80\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\xf6\xd6\x00\x01\x00\x00\x00\x00\xd3-\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\...
> e | [0, 0, 400, 300]
> flag | False
> fp | <_io.BytesIO object at 0x7f67f5a86710>
> im | <PIL.Jpeg2KImagePlugin.Jpeg2KImageFile image mode=RGB size=400x300 at 0x7F67F5C7EA90>
> o | 0
> self | <PIL.ImageFile.Parser object at 0x7f67f5d8a290>
> ```
> File "PIL/Image.py", line 435, in _getdecoder
> return decoder(mode, *args + extra)
> ```
> args | ['jp2', 0, 0, -1, 1024, <_io.BytesIO object at 0x7f67f5a86710>]
> decoder | <built-in function jpeg2k_decoder>
> decoder_name | 'jpeg2k'
> extra | []
> mode | 'RGB'
> ```
On PyImaging_Jpeg2KDecoderNew
| [
{
"content": "#\n# The Python Imaging Library\n# $Id$\n#\n# JPEG2000 file handling\n#\n# History:\n# 2014-03-12 ajh Created\n#\n# Copyright (c) 2014 Coriolis Systems Limited\n# Copyright (c) 2014 Alastair Houghton\n#\n# See the README file for information on usage and redistribution.\n#\nfrom . import Image, I... | [
{
"content": "#\n# The Python Imaging Library\n# $Id$\n#\n# JPEG2000 file handling\n#\n# History:\n# 2014-03-12 ajh Created\n#\n# Copyright (c) 2014 Coriolis Systems Limited\n# Copyright (c) 2014 Alastair Houghton\n#\n# See the README file for information on usage and redistribution.\n#\nfrom . import Image, I... | diff --git a/Tests/test_file_jpeg2k.py b/Tests/test_file_jpeg2k.py
index 0766f5b07c9..810e21a9d74 100644
--- a/Tests/test_file_jpeg2k.py
+++ b/Tests/test_file_jpeg2k.py
@@ -176,6 +176,19 @@ def test_unbound_local(self):
with self.assertRaises(IOError):
Image.open('Tests/images/unbound_variable.jp2')
+ def test_parser_feed(self):
+ # Arrange
+ from PIL import ImageFile
+ with open('Tests/images/test-card-lossless.jp2', 'rb') as f:
+ data = f.read()
+
+ # Act
+ p = ImageFile.Parser()
+ p.feed(data)
+
+ # Assert
+ self.assertEqual(p.image.size, (640, 480))
+
if __name__ == '__main__':
unittest.main()
diff --git a/src/PIL/Jpeg2KImagePlugin.py b/src/PIL/Jpeg2KImagePlugin.py
index 4619cc5231c..7ab183a4b3a 100644
--- a/src/PIL/Jpeg2KImagePlugin.py
+++ b/src/PIL/Jpeg2KImagePlugin.py
@@ -192,7 +192,7 @@ def _open(self):
length = -1
self.tile = [('jpeg2k', (0, 0) + self.size, 0,
- (self.codec, self.reduce, self.layers, fd, length, self.fp))]
+ (self.codec, self.reduce, self.layers, fd, length))]
def load(self):
if self.reduce:
|
pypa__pipenv-2699 | Pipenv breaks with pre-existing usage of .venv files
#### Issue description
tl;dr `.venv` files already exist in some dev setups but when present, `pipenv` attempts to use that file as a directory and install a virtualenv into it.
Virtualenvwrapper has suggested for awhile to use `.venv` files in your project directory to hold the name of your virtualenv so that you could automatically activate virtualenvs. Spacemacs (an Emacs editor setup) also does this behavior by default.
Two problems:
1. Pipenv will silently fail to create and activate a virtualenv when a `.venv` file is present.
2. If a `.venv` file is added to an existing pipenv project, pipenv breaks because it can't activate the virtualenv at `<project_dir>/.venv` ignoring the place where it had already created a virtualenv.
Prior Art Links:
- Virtualenvwrapper Tips & Tricks: http://virtualenvwrapper.readthedocs.io/en/latest/tips.html
- Spacemacs Python Layer: https://github.com/syl20bnr/spacemacs/tree/develop/layers/%2Blang/python#automatic-activation-of-local-pyenv-version
- Virtualenv in git directories: https://hmarr.com/2010/jan/19/making-virtualenv-play-nice-with-git/
- Sample .bash_profile using `.venv` files: https://github.com/justinabrahms/jlilly-bashy-dotfiles/commit/04899f005397499e89da6d562b062545e70d7975#commitcomment-1526375
#### Expected result
At the least, Pipenv should check if `.venv` is a file or a directory before attempting to automatically use it as a directory and install a virtualenv into it. If it's a file, then behave as if no `.venv` was found at all.
#### Actual result
If a `.venv` file exists in the project directory, Pipenv will attempt to create and use a virtualenv at `<project_dir>/.venv` and yell at the user a bit because it's not a directory but (a) it won't actually fail and (b) it won't actually save a virtualenv _anywhere_.
Output:
```
Creating a virtualenv for this project...
Pipfile: <project_path>\Pipfile
Using <python_path> to create virtualenv...
Running virtualenv with interpreter <python_path>
ERROR: File already exists and is not a directory.
Please provide a different path or delete the file.
Virtualenv location: <project_path>\.venv
```
#### Steps to replicate
`touch .venv`
`pipenv install`
Also see output of `pipenv --venv`
-------------------------------------------------------------------------------
<details><summary>$ pipenv --support</summary>
*REDACTED*
Pipenv version: `'2018.7.1'`
PEP 508 Information:
```
{'implementation_name': 'cpython',
'implementation_version': '3.6.5',
'os_name': 'nt',
'platform_machine': 'AMD64',
'platform_python_implementation': 'CPython',
'platform_release': '10',
'platform_system': 'Windows',
'platform_version': '10.0.17134',
'python_full_version': '3.6.5',
'python_version': '3.6',
'sys_platform': 'win32'}
```
System environment variables:
- `ADSK_CLM_WPAD_PROXY_CHECK`
- `ALIASES`
- `ALLUSERSPROFILE`
- `ANSICON`
- `ANSICON_DEF`
- `APPDATA`
- `ARCHITECTURE`
- `CHOCOLATEYINSTALL`
- `CHOCOLATEYLASTPATHUPDATE`
- `CMDER_ROOT`
- `COMMONPROGRAMFILES`
- `COMMONPROGRAMFILES(X86)`
- `COMMONPROGRAMW6432`
- `COMPUTERNAME`
- `COMSPEC`
- `CONEMUANSI`
- `CONEMUANSILOG`
- `CONEMUARGS`
- `CONEMUARGS2`
- `CONEMUBACKHWND`
- `CONEMUBASEDIR`
- `CONEMUBASEDIRSHORT`
- `CONEMUBUILD`
- `CONEMUCFGDIR`
- `CONEMUCONFIG`
- `CONEMUDIR`
- `CONEMUDRAWHWND`
- `CONEMUDRIVE`
- `CONEMUHOOKS`
- `CONEMUHWND`
- `CONEMUPALETTE`
- `CONEMUPID`
- `CONEMUSERVERPID`
- `CONEMUTASK`
- `CONEMUWORKDIR`
- `CONEMUWORKDRIVE`
- `DRIVERDATA`
- `FPS_BROWSER_APP_PROFILE_STRING`
- `FPS_BROWSER_USER_PROFILE_STRING`
- `FSHARPINSTALLDIR`
- `GIT_INSTALL_ROOT`
- `GOPATH`
- `GOROOT`
- `GTK_BASEPATH`
- `HOME`
- `HOMEDRIVE`
- `HOMEPATH`
- `LOCALAPPDATA`
- `LOGONSERVER`
- `NUMBER_OF_PROCESSORS`
- `ONEDRIVE`
- `OS`
- `PATH`
- `PATHEXT`
- `PLINK_PROTOCOL`
- `PROCESSOR_ARCHITECTURE`
- `PROCESSOR_IDENTIFIER`
- `PROCESSOR_LEVEL`
- `PROCESSOR_REVISION`
- `PROGRAMDATA`
- `PROGRAMFILES`
- `PROGRAMFILES(X86)`
- `PROGRAMW6432`
- `PROMPT`
- `PSMODULEPATH`
- `PUBLIC`
- `RUBYOPT`
- `SESSIONNAME`
- `SVN_SSH`
- `SYSTEMDRIVE`
- `SYSTEMROOT`
- `TEMP`
- `TERM`
- `TMP`
- `USER-ALIASES`
- `USERDOMAIN`
- `USERDOMAIN_ROAMINGPROFILE`
- `USERNAME`
- `USERPROFILE`
- `VERBOSE-OUTPUT`
- `WINDIR`
- `WORKON_HOME`
- `PYTHONDONTWRITEBYTECODE`
- `PIP_PYTHON_PATH`
Pipenvûspecific environment variables:
---------------------------
Contents of `Pipfile`:
```toml
[[source]]
url = "https://pypi.org/simple"
verify_ssl = true
name = "pypi"
[scripts]
server = "python manage.py runserver"
migrate = "python manage.py migrate"
test = "python manage.py test"
[packages]
"boto3" = "==1.7"
dj-database-url = "==0.5.0"
json-logging-py = "*"
"psycopg2" = "==2.7"
pytz = "==2018.4"
requests = "==2.18.4"
Django = "==2.0.6"
django_csp = "==3.4"
"Jinja2" = "==2.10"
djangorestframework = "*"
[dev-packages]
ipython = "*"
pylint = "*"
pylint-django = "*"
pylint-plugin-utils = "*"
autoflake = "*"
importmagic = "*"
epc = "*"
[requires]
python_version = "3.6"
```
</details>
| [
{
"content": "# -*- coding: utf-8 -*-\nimport io\nimport json\nimport os\nimport re\nimport sys\nimport base64\nimport fnmatch\nimport hashlib\nimport contoml\nfrom first import first\nimport pipfile\nimport pipfile.api\nimport six\nimport toml\n\nfrom ._compat import Path\n\nfrom .cmdparse import Script\nfrom ... | [
{
"content": "# -*- coding: utf-8 -*-\nimport io\nimport json\nimport os\nimport re\nimport sys\nimport base64\nimport fnmatch\nimport hashlib\nimport contoml\nfrom first import first\nimport pipfile\nimport pipfile.api\nimport six\nimport toml\n\nfrom ._compat import Path\n\nfrom .cmdparse import Script\nfrom ... | diff --git a/news/2680.bugfix b/news/2680.bugfix
new file mode 100644
index 0000000000..405fb1dd80
--- /dev/null
+++ b/news/2680.bugfix
@@ -0,0 +1 @@
+Fixed virtualenv creation failure when a .venv file is present in the project root.
diff --git a/pipenv/project.py b/pipenv/project.py
index c5f451e495..e32296e521 100644
--- a/pipenv/project.py
+++ b/pipenv/project.py
@@ -249,7 +249,7 @@ def requirements_exists(self):
def is_venv_in_project(self):
return PIPENV_VENV_IN_PROJECT or (
self.project_directory
- and os.path.exists(os.path.join(self.project_directory, ".venv"))
+ and os.path.isdir(os.path.join(self.project_directory, ".venv"))
)
@property
diff --git a/tests/integration/test_dot_venv.py b/tests/integration/test_dot_venv.py
index e7b157f3c2..2063701b43 100644
--- a/tests/integration/test_dot_venv.py
+++ b/tests/integration/test_dot_venv.py
@@ -1,5 +1,6 @@
import os
+from pipenv._compat import TemporaryDirectory, Path
from pipenv.project import Project
from pipenv.utils import temp_environ, normalize_drive, get_windows_path
from pipenv.vendor import delegator
@@ -39,3 +40,30 @@ def test_reuse_previous_venv(PipenvInstance, pypi):
c = p.pipenv('install requests')
assert c.return_code == 0
assert normalize_drive(p.path) in p.pipenv('--venv').out
+
+@pytest.mark.dotvenv
+def test_venv_file_exists(PipenvInstance, pypi):
+ """Tests virtualenv creation & package installation when a .venv file exists
+ at the project root.
+ """
+ with PipenvInstance(pypi=pypi, chdir=True) as p:
+ file_path = os.path.join(p.path, '.venv')
+ with open(file_path, 'w') as f:
+ f.write('')
+
+ with temp_environ(), TemporaryDirectory(
+ prefix='pipenv-', suffix='temp_workon_home'
+ ) as workon_home:
+ os.environ['WORKON_HOME'] = workon_home.name
+ if 'PIPENV_VENV_IN_PROJECT' in os.environ:
+ del os.environ['PIPENV_VENV_IN_PROJECT']
+
+ c = p.pipenv('install requests')
+ assert c.return_code == 0
+
+ venv_loc = None
+ for line in c.err.splitlines():
+ if line.startswith('Virtualenv location:'):
+ venv_loc = Path(line.split(':', 1)[-1].strip())
+ assert venv_loc is not None
+ assert venv_loc.joinpath('.project').exists()
|
mathesar-foundation__mathesar-940 | DB Types in column.valid_target_types are not in sync with the types returned in database types endpoint
## Description
* `valid_target_types` property of column returns "DOUBLE PRECISION"
- Endpoint: /api/v0/tables/14/columns/
* Types endpoint returns mathesar types where Number has the db type "DOUBLE_PRECISION"
- http://localhost:8000/api/v0/databases/1/types/
- Mathesar type: Number
Note that "DOUBLE PRECISION" and "DOUBLE_PRECISION" differ from each other.
## Expected behavior
Both endpoints should return values with same spelling.
| [
{
"content": "from enum import Enum\n\nfrom sqlalchemy import create_engine\n\nfrom db import constants\n\n\nCHAR = 'char'\nSTRING = 'string'\nVARCHAR = 'varchar'\n\n\nclass PostgresType(Enum):\n \"\"\"\n This only includes built-in Postgres types that SQLAlchemy supports.\n SQLAlchemy doesn't support ... | [
{
"content": "from enum import Enum\n\nfrom sqlalchemy import create_engine\n\nfrom db import constants\n\n\nCHAR = 'char'\nSTRING = 'string'\nVARCHAR = 'varchar'\n\n\nclass PostgresType(Enum):\n \"\"\"\n This only includes built-in Postgres types that SQLAlchemy supports.\n SQLAlchemy doesn't support ... | diff --git a/db/tests/columns/operations/test_alter.py b/db/tests/columns/operations/test_alter.py
index 3699586897..c047a9cdd8 100644
--- a/db/tests/columns/operations/test_alter.py
+++ b/db/tests/columns/operations/test_alter.py
@@ -425,7 +425,7 @@ def test_batch_update_columns_no_changes(engine_email_type):
assert len(table.columns) == len(updated_table.columns)
for index, column in enumerate(table.columns):
- new_column_type = get_db_type_name(updated_table.columns[index].type, engine_email_type)
+ new_column_type = get_db_type_name(updated_table.columns[index].type, engine)
assert new_column_type == 'VARCHAR'
assert updated_table.columns[index].name == table.columns[index].name
@@ -436,15 +436,15 @@ def test_batch_update_column_names(engine_email_type):
table_oid = get_oid_from_table(table.name, schema, engine)
column_data = _get_pizza_column_data()
- column_data[1]['name'] == 'Pizza Style'
- column_data[2]['name'] == 'Eaten Recently?'
+ column_data[1]['name'] = 'Pizza Style'
+ column_data[2]['name'] = 'Eaten Recently?'
batch_update_columns(table_oid, engine, column_data)
updated_table = reflect_table(table.name, schema, engine)
assert len(table.columns) == len(updated_table.columns)
for index, column in enumerate(table.columns):
- new_column_type = get_db_type_name(updated_table.columns[index].type, engine_email_type)
+ new_column_type = get_db_type_name(updated_table.columns[index].type, engine)
assert new_column_type == column_data[index]['plain_type']
assert updated_table.columns[index].name == column_data[index]['name']
@@ -455,15 +455,15 @@ def test_batch_update_column_types(engine_email_type):
table_oid = get_oid_from_table(table.name, schema, engine)
column_data = _get_pizza_column_data()
- column_data[0]['plain_type'] == 'INTEGER'
- column_data[2]['plain_type'] == 'BOOLEAN'
+ column_data[0]['plain_type'] = 'DOUBLE PRECISION'
+ column_data[2]['plain_type'] = 'BOOLEAN'
batch_update_columns(table_oid, engine, column_data)
updated_table = reflect_table(table.name, schema, engine)
assert len(table.columns) == len(updated_table.columns)
for index, column in enumerate(table.columns):
- new_column_type = get_db_type_name(updated_table.columns[index].type, engine_email_type)
+ new_column_type = get_db_type_name(updated_table.columns[index].type, engine)
assert new_column_type == column_data[index]['plain_type']
assert updated_table.columns[index].name == column_data[index]['name']
@@ -474,17 +474,17 @@ def test_batch_update_column_names_and_types(engine_email_type):
table_oid = get_oid_from_table(table.name, schema, engine)
column_data = _get_pizza_column_data()
- column_data[0]['name'] == 'Pizza ID'
- column_data[0]['plain_type'] == 'INTEGER'
- column_data[1]['name'] == 'Pizza Style'
- column_data[2]['plain_type'] == 'BOOLEAN'
+ column_data[0]['name'] = 'Pizza ID'
+ column_data[0]['plain_type'] = 'INTEGER'
+ column_data[1]['name'] = 'Pizza Style'
+ column_data[2]['plain_type'] = 'BOOLEAN'
batch_update_columns(table_oid, engine, column_data)
updated_table = reflect_table(table.name, schema, engine)
assert len(table.columns) == len(updated_table.columns)
for index, column in enumerate(table.columns):
- new_column_type = get_db_type_name(updated_table.columns[index].type, engine_email_type)
+ new_column_type = get_db_type_name(updated_table.columns[index].type, engine)
assert new_column_type == column_data[index]['plain_type']
assert updated_table.columns[index].name == column_data[index]['name']
@@ -503,7 +503,7 @@ def test_batch_update_column_drop_columns(engine_email_type):
assert len(updated_table.columns) == len(table.columns) - 2
for index, column in enumerate(updated_table.columns):
- new_column_type = get_db_type_name(updated_table.columns[index].type, engine_email_type)
+ new_column_type = get_db_type_name(updated_table.columns[index].type, engine)
assert new_column_type == column_data[index - 2]['plain_type']
assert updated_table.columns[index].name == column_data[index - 2]['name']
@@ -525,6 +525,6 @@ def test_batch_update_column_all_operations(engine_email_type):
assert len(updated_table.columns) == len(table.columns) - 1
for index, column in enumerate(updated_table.columns):
- new_column_type = get_db_type_name(updated_table.columns[index].type, engine_email_type)
+ new_column_type = get_db_type_name(updated_table.columns[index].type, engine)
assert new_column_type == column_data[index]['plain_type']
assert updated_table.columns[index].name == column_data[index]['name']
diff --git a/db/types/base.py b/db/types/base.py
index 1b410c00b1..eb04f5ccc4 100644
--- a/db/types/base.py
+++ b/db/types/base.py
@@ -87,8 +87,8 @@ def get_available_types(engine):
def get_db_type_name(sa_type, engine):
- USER_DEFINED_STR = 'user_defined'
- db_type = sa_type.__visit_name__
- if db_type == USER_DEFINED_STR:
- db_type = sa_type().compile(engine.dialect)
+ try:
+ db_type = sa_type.compile(dialect=engine.dialect)
+ except TypeError:
+ db_type = sa_type().compile(dialect=engine.dialect)
return db_type
|
bokeh__bokeh-6022 | sdists prompting for BokehJS build will block pip installs
I am currently trying to run a python file on a remote server. In my local machine I can just ran the command: bokeh serve --show myApp.py
However, on my remote host, when I ran bokeh serve, the error message "bokeh: command not found" was shown. I tried pip install bokeh, and finished installation, however, still bokeh command is not found.
| [
{
"content": "'''\n\n'''\nfrom __future__ import print_function\n\nimport shutil\nfrom os.path import dirname, exists, join, realpath, relpath\nimport os, re, subprocess, sys, time\n\nimport versioneer\n\n# provide fallbacks for highlights in case colorama is not installed\ntry:\n import colorama\n from c... | [
{
"content": "'''\n\n'''\nfrom __future__ import print_function\n\nimport shutil\nfrom os.path import dirname, exists, join, realpath, relpath\nimport os, re, subprocess, sys, time\n\nimport versioneer\n\n# provide fallbacks for highlights in case colorama is not installed\ntry:\n import colorama\n from c... | diff --git a/_setup_support.py b/_setup_support.py
index d039f848b8a..dbd0d908d41 100644
--- a/_setup_support.py
+++ b/_setup_support.py
@@ -182,7 +182,7 @@ def fixup_for_packaged():
None
'''
- if exists(join(ROOT, 'PKG-INFOvi ')):
+ if exists(join(ROOT, 'PKG-INFO')):
if "--build-js" in sys.argv or "--install-js" in sys.argv:
print(SDIST_BUILD_WARNING)
if "--build-js" in sys.argv:
|
vispy__vispy-1113 | Buggy display with iso volume example
Using `examples/basics/scene/volume.py` and switching to iso rendering method produces a [pretty strange view](http://youtu.be/3becSPxKIq8).
System is the same as before (Intel HD 4600):
```
Platform: Linux-4.0.4-301.fc22.x86_64-x86_64-with-fedora-22-Twenty_Two
Python: 3.4.2 (default, Jan 12 2015, 12:13:20) [GCC 4.9.2 20150107 (Red Hat 4.9.2-5)]
Backend: PyQt4
pyqt4: ('PyQt4', '4.11.3', '4.8.6')
pyqt5: None
pyside: None
pyglet: pyglet 1.2.1
glfw: None
sdl2: None
wx: None
egl: EGL 1.4 (DRI2) Mesa Project: OpenGL OpenGL_ES OpenGL_ES2 OpenGL_ES3
_test: None
GL version: '3.0 Mesa 10.5.4'
MAX_TEXTURE_SIZE: 8192
```
| [
{
"content": "# -*- coding: utf-8 -*-\n# Copyright (c) 2015, Vispy Development Team.\n# Distributed under the (new) BSD License. See LICENSE.txt for more info.\n\n\"\"\"\nAbout this technique\n--------------------\n\nIn Python, we define the six faces of a cuboid to draw, as well as\ntexture cooridnates corresp... | [
{
"content": "# -*- coding: utf-8 -*-\n# Copyright (c) 2015, Vispy Development Team.\n# Distributed under the (new) BSD License. See LICENSE.txt for more info.\n\n\"\"\"\nAbout this technique\n--------------------\n\nIn Python, we define the six faces of a cuboid to draw, as well as\ntexture cooridnates corresp... | diff --git a/vispy/visuals/volume.py b/vispy/visuals/volume.py
index d840e83c9b..c2ffab2ad8 100644
--- a/vispy/visuals/volume.py
+++ b/vispy/visuals/volume.py
@@ -335,6 +335,7 @@
before_loop="""
vec4 color3 = vec4(0.0); // final color
vec3 dstep = 1.5 / u_shape; // step to sample derivative
+ gl_FragColor = vec4(0.0);
""",
in_loop="""
if (val > u_threshold-0.2) {
|
ultralytics__yolov5-296 | TypeError: can't pickle torch.distributed.ProcessGroupNCCL objects
Hi，
I meet a problem:
Traceback (most recent call last):
File "train.py", line 394, in <module>
train(hyp)
File "train.py", line 331, in train
torch.save(ckpt, last)
File "/home/yy/anaconda3/lib/python3.6/site-packages/torch/serialization.py", line 328, in save
_legacy_save(obj, opened_file, pickle_module, pickle_protocol)
File "/home/yy/anaconda3/lib/python3.6/site-packages/torch/serialization.py", line 401, in _legacy_save
pickler.dump(obj)
**TypeError: can't pickle torch.distributed.ProcessGroupNCCL objects**
Thanks!
environment:
ubuntu 16.04
GPU 2080Ti *4
pytorch 1.4.0
| [
{
"content": "import math\nimport os\nimport time\nfrom copy import deepcopy\n\nimport torch\nimport torch.backends.cudnn as cudnn\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision.models as models\n\n\ndef init_seeds(seed=0):\n torch.manual_seed(seed)\n\n # Speed-reproducibility... | [
{
"content": "import math\nimport os\nimport time\nfrom copy import deepcopy\n\nimport torch\nimport torch.backends.cudnn as cudnn\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision.models as models\n\n\ndef init_seeds(seed=0):\n torch.manual_seed(seed)\n\n # Speed-reproducibility... | diff --git a/utils/torch_utils.py b/utils/torch_utils.py
index dd2e6e75ff97..fd00b8bde080 100644
--- a/utils/torch_utils.py
+++ b/utils/torch_utils.py
@@ -201,5 +201,5 @@ def update(self, model):
def update_attr(self, model):
# Update EMA attributes
for k, v in model.__dict__.items():
- if not k.startswith('_') and k != 'module':
+ if not k.startswith('_') and k not in ["process_group", "reducer"]:
setattr(self.ema, k, v)
|
pypa__cibuildwheel-1282 | --only doesn't work for `-win32` identifiers
> - cp311 https://github.com/ddelange/asyncpg/actions/runs/3092263953/jobs/5003341864#step:4:43
> - same for all other win32 builds: `Invalid --only='cp310-win32', must be a build selector with a known platform`
>
> in https://github.com/ddelange/asyncpg/pull/2
>
> and before this change it was fine: https://github.com/ddelange/asyncpg/actions/runs/3072698535/jobs/4964355341#step:4:89
>
> maybe it's an issue with the `--only` flag?
>
> on my mac:
>
> ```console
> $ cibuildwheel --print-build-identifiers --platform windows --arch x86,AMD64
> cp36-win32
> cp36-win_amd64
> cp37-win32
> cp37-win_amd64
> cp38-win32
> cp38-win_amd64
> cp39-win32
> cp39-win_amd64
> cp310-win32
> cp310-win_amd64
> cp311-win32
> cp311-win_amd64
> pp37-win_amd64
> pp38-win_amd64
> pp39-win_amd64
> ```
>
_Originally posted by @ddelange in https://github.com/pypa/cibuildwheel/issues/1266#issuecomment-1252703994_
| [
{
"content": "from __future__ import annotations\n\nimport argparse\nimport os\nimport shutil\nimport sys\nimport tarfile\nimport textwrap\nimport typing\nfrom pathlib import Path\nfrom tempfile import mkdtemp\n\nimport cibuildwheel\nimport cibuildwheel.linux\nimport cibuildwheel.macos\nimport cibuildwheel.util... | [
{
"content": "from __future__ import annotations\n\nimport argparse\nimport os\nimport shutil\nimport sys\nimport tarfile\nimport textwrap\nimport typing\nfrom pathlib import Path\nfrom tempfile import mkdtemp\n\nimport cibuildwheel\nimport cibuildwheel.linux\nimport cibuildwheel.macos\nimport cibuildwheel.util... | diff --git a/cibuildwheel/__main__.py b/cibuildwheel/__main__.py
index 4d1402d8c..dcfc81d46 100644
--- a/cibuildwheel/__main__.py
+++ b/cibuildwheel/__main__.py
@@ -173,7 +173,7 @@ def build_in_directory(args: CommandLineArguments) -> None:
platform = "linux"
elif "macosx_" in args.only:
platform = "macos"
- elif "win_" in args.only:
+ elif "win_" in args.only or "win32" in args.only:
platform = "windows"
else:
print(
diff --git a/unit_test/main_tests/main_platform_test.py b/unit_test/main_tests/main_platform_test.py
index 9136802c8..b947ce4f5 100644
--- a/unit_test/main_tests/main_platform_test.py
+++ b/unit_test/main_tests/main_platform_test.py
@@ -199,6 +199,7 @@ def test_archs_platform_all(platform, intercepted_build_args, monkeypatch):
(
("cp311-manylinux_x86_64", "linux"),
("cp310-win_amd64", "windows"),
+ ("cp310-win32", "windows"),
("cp311-macosx_x86_64", "macos"),
),
)
|
openai__openai-python-1007 | Missing default value to logprobs in openai.types.chat.chat_completion.Choice
### Confirm this is an issue with the Python library and not an underlying OpenAI API
- [X] This is an issue with the Python library
### Describe the bug
#980 added token `logprobs` to chat completions of type `Optional[ChoiceLogprobs]` in [`openai.types.chat.chat_completion.Choice`](https://github.com/openai/openai-python/blob/3ad4e8bc9d89d7a81586bf598289ff62b0a339b9/src/openai/types/chat/chat_completion.py#L33) and [`openai.types.chat.chat_completion_chunk.Choice`](https://github.com/openai/openai-python/blob/3ad4e8bc9d89d7a81586bf598289ff62b0a339b9/src/openai/types/chat/chat_completion_chunk.py#L97). In the latter, the default value is set to `None`, while in the former it is not set. This causes backward compatibility problems with code written for versions prior to 1.5.0.
### To Reproduce
Execution of the following code fails:
```python
from openai.types.chat.chat_completion import ChatCompletionMessage, Choice
msg = ChatCompletionMessage(role="assistant", content="")
Choice(
index=0,
finish_reason="stop",
message=msg,
)
```
The output
```
----> 1 Choice(
2 index=0,
3 finish_reason="stop",
4 message=msg,
5 )
File /.venv-3.10/lib/python3.10/site-packages/pydantic/main.py:164, in BaseModel.__init__(__pydantic_self__, **data)
162 # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
163 __tracebackhide__ = True
--> 164 __pydantic_self__.__pydantic_validator__.validate_python(data, self_instance=__pydantic_self__)
ValidationError: 1 validation error for Choice
logprobs
Field required [type=missing, input_value={'index': 0, 'finish_reas...=None, tool_calls=None)}, input_type=dict]
For further information visit https://errors.pydantic.dev/2.5/v/missing
```
Setting `logprobs` to `None` fixes the problem.
```python
from openai.types.chat.chat_completion import ChatCompletionMessage, Choice
msg = ChatCompletionMessage(role="assistant", content="")
Choice(
index=0,
finish_reason="stop",
message=msg,
logprobs=None # added line
)
```
### Code snippets
```Python
see above
```
### OS
Linux
### Python version
Python 3.10.13
### Library version
openai 1.6.0
| [
{
"content": "# File generated from our OpenAPI spec by Stainless.\n\nfrom typing import List, Optional\nfrom typing_extensions import Literal\n\nfrom ..._models import BaseModel\nfrom ..completion_usage import CompletionUsage\nfrom .chat_completion_message import ChatCompletionMessage\nfrom .chat_completion_to... | [
{
"content": "# File generated from our OpenAPI spec by Stainless.\n\nfrom typing import List, Optional\nfrom typing_extensions import Literal\n\nfrom ..._models import BaseModel\nfrom ..completion_usage import CompletionUsage\nfrom .chat_completion_message import ChatCompletionMessage\nfrom .chat_completion_to... | diff --git a/src/openai/types/chat/chat_completion.py b/src/openai/types/chat/chat_completion.py
index 055280c347..b2e98a3144 100644
--- a/src/openai/types/chat/chat_completion.py
+++ b/src/openai/types/chat/chat_completion.py
@@ -30,7 +30,7 @@ class Choice(BaseModel):
index: int
"""The index of the choice in the list of choices."""
- logprobs: Optional[ChoiceLogprobs]
+ logprobs: Optional[ChoiceLogprobs] = None
"""Log probability information for the choice."""
message: ChatCompletionMessage
|
freedomofpress__securedrop-703 | Don't armor encrypted submissions
SecureDrop currently armors encrypted submissions. This bloats the size of stored submissions significantly due to the encoding. For example, a 93 MB upload results in a 125.7 MB submission for the journalist to download.
Downloading anything over Tor is very slow (the aforementioned download took me, on average, 9 minutes to download). Therefore, unnecessarily increasing the size of submissions severely impacts usability. There is no reason that I can think of to ascii armor submissions - they are uploaded and downloaded over HTTP, which automatically handles encoding and de-encoding binary data.
| [
{
"content": "# -*- coding: utf-8 -*-\nimport os\nimport subprocess\nfrom base64 import b32encode\n\nfrom Crypto.Random import random\nimport gnupg\nimport scrypt\n\nimport config\nimport store\n\n# to fix gpg error #78 on production\nos.environ['USERNAME'] = 'www-data'\n\nGPG_KEY_TYPE = \"RSA\"\nif os.environ.... | [
{
"content": "# -*- coding: utf-8 -*-\nimport os\nimport subprocess\nfrom base64 import b32encode\n\nfrom Crypto.Random import random\nimport gnupg\nimport scrypt\n\nimport config\nimport store\n\n# to fix gpg error #78 on production\nos.environ['USERNAME'] = 'www-data'\n\nGPG_KEY_TYPE = \"RSA\"\nif os.environ.... | diff --git a/securedrop/crypto_util.py b/securedrop/crypto_util.py
index c965484675..786828c168 100644
--- a/securedrop/crypto_util.py
+++ b/securedrop/crypto_util.py
@@ -158,7 +158,8 @@ def encrypt(plaintext, fingerprints, output=None):
out = encrypt_fn(plaintext,
*fingerprints,
output=output,
- always_trust=True)
+ always_trust=True,
+ armor=False)
if out.ok:
return out.data
else:
diff --git a/securedrop/tests/test_unit_integration.py b/securedrop/tests/test_unit_integration.py
index 64bedf51d9..d1ccc152b2 100644
--- a/securedrop/tests/test_unit_integration.py
+++ b/securedrop/tests/test_unit_integration.py
@@ -277,7 +277,7 @@ def _can_decrypt_with_key(self, msg, key_fpr, passphrase=None):
salt=crypto_util.SCRYPT_GPG_PEPPER)
decrypted_data = gpg.decrypt(msg, passphrase=passphrase)
self.assertTrue(decrypted_data.ok,
- "Could not decrypt msg with key, gpg says: {}".format(decrypted_data.status))
+ "Could not decrypt msg with key, gpg says: {}".format(decrypted_data.stderr))
# We have to clean up the temporary GPG dir
shutil.rmtree(gpg_tmp_dir)
@@ -362,10 +362,10 @@ def helper_test_reply(self, test_reply, expected_success=True):
doc_names_selected=checkbox_values
), follow_redirects=True)
self.assertEqual(rv.status_code, 200)
- pgp_msg_re = r'-----BEGIN PGP MESSAGE-----.*-----END PGP MESSAGE-----'
- pgp_msg = re.search(pgp_msg_re, rv.data, re.MULTILINE|re.DOTALL).group(0)
- self._can_decrypt_with_key(pgp_msg, config.JOURNALIST_KEY)
- self._can_decrypt_with_key(pgp_msg, crypto_util.getkey(sid), codename)
+ zf = zipfile.ZipFile(StringIO(rv.data), 'r')
+ data = zf.read(zf.namelist()[0])
+ self._can_decrypt_with_key(data, config.JOURNALIST_KEY)
+ self._can_decrypt_with_key(data, crypto_util.getkey(sid), codename)
# Test deleting reply on the journalist interface
last_reply_number = len(soup.select('input[name="doc_names_selected"]')) - 1
|
scikit-hep__pyhf-307 | Add --version flag to pyhf CLI
# Description
As [suggested by Lukas](https://github.com/diana-hep/pyhf/pull/304#issuecomment-428856809), adding a `--version` flag to the pyhf CLI could be useful.
| [
{
"content": "import logging\nlogging.basicConfig()\nlog = logging.getLogger(__name__)\n\nimport click\nimport json\nimport os\nimport jsonpatch\nimport sys\n\nfrom . import readxml\nfrom . import writexml\nfrom .utils import runOnePoint\nfrom .pdf import Model\n\n\n@click.group(context_settings=dict(help_optio... | [
{
"content": "import logging\nlogging.basicConfig()\nlog = logging.getLogger(__name__)\n\nimport click\nimport json\nimport os\nimport jsonpatch\nimport sys\n\nfrom . import readxml\nfrom . import writexml\nfrom .utils import runOnePoint\nfrom .pdf import Model\nfrom .version import __version__\n\n\n@click.grou... | diff --git a/pyhf/commandline.py b/pyhf/commandline.py
index beeaff70fc..2b8bad4d91 100644
--- a/pyhf/commandline.py
+++ b/pyhf/commandline.py
@@ -12,9 +12,11 @@
from . import writexml
from .utils import runOnePoint
from .pdf import Model
+from .version import __version__
@click.group(context_settings=dict(help_option_names=['-h', '--help']))
+@click.version_option(version=__version__)
def pyhf():
pass
|
mars-project__mars-274 | [BUG] Tensor does not return data when eager mode is on
<!--
Thank you for your contribution!
Please review https://github.com/mars-project/mars/blob/master/CONTRIBUTING.rst before opening an issue.
-->
**Describe the bug**
First, I execute a tensor with eager mode off, it succeeded, then I turn on eager mode, execute another tensor with some error happened, finally, repr the identical tensor to the first tensor does not trigger execution automatically.
**To Reproduce**
```
In [1]: import mars.tensor as mt
In [2]: from mars.config import options
In [3]: a = mt.array(
...: [[0.1, 0.2, 0.3],
...: [0.3, 0.4, 0.2]]
...: )
In [4]: (a[:, mt.newaxis, :] - a[mt.newaxis, ...]).execute()
Out[4]:
array([[[ 0. , 0. , 0. ],
[-0.2, -0.2, 0.1]],
[[ 0.2, 0.2, -0.1],
[ 0. , 0. , 0. ]]])
In [5]: options.eager_mode = True
In [6]: mt.sqrt(mt.sum((a[:, mt.newaxis, :] - a[mt.newaxis, ...]) ** 2, axis=-1))
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
~/Workspace/mars/mars/tiles.py in _dispatch(self, op)
110 try:
--> 111 handler = self._handlers[op_cls]
112 return handler(op)
KeyError: <class 'mars.tensor.expressions.arithmetic.sqrt.TensorSqrt'>
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-6-de975d9f2cf8> in <module>
----> 1 mt.sqrt(mt.sum((a[:, mt.newaxis, :] - a[mt.newaxis, ...]) ** 2, axis=-1))
~/Workspace/mars/mars/tensor/expressions/utils.py in h(*tensors, **kw)
157 kw['dtype'] = dtype
158
--> 159 ret = func(*tensors, **kw)
160 if ret is NotImplemented:
161 reverse_func = getattr(inspect.getmodule(func), 'r{0}'.format(func.__name__), None) \
~/Workspace/mars/mars/tensor/expressions/arithmetic/sqrt.py in sqrt(x, out, where, **kwargs)
78 """
79 op = TensorSqrt(**kwargs)
---> 80 return op(x, out=out, where=where)
81
~/Workspace/mars/mars/tensor/expressions/arithmetic/core.py in __call__(self, x, out, where)
348
349 def __call__(self, x, out=None, where=None):
--> 350 return self._call(x, out=out, where=where)
351
352
~/Workspace/mars/mars/tensor/expressions/arithmetic/core.py in _call(self, x, out, where)
331 shape = x.shape
332
--> 333 t = self.new_tensor([x, out, where], shape)
334
335 if out is None:
~/Workspace/mars/mars/tensor/expressions/core.py in new_tensor(self, inputs, shape, dtype, **kw)
54 raise TypeError('cannot new tensor with more than 1 outputs')
55
---> 56 return self.new_tensors(inputs, shape, dtype=dtype, **kw)[0]
57
58 def calc_shape(self, *inputs_shape):
~/Workspace/mars/mars/tensor/expressions/core.py in new_tensors(self, inputs, shape, dtype, chunks, nsplits, output_limit, kws, **kw)
48 output_limit=None, kws=None, **kw):
49 return self.new_entities(inputs, shape, chunks=chunks, nsplits=nsplits,
---> 50 output_limit=output_limit, kws=kws, dtype=dtype, **kw)
51
52 def new_tensor(self, inputs, shape, dtype=None, **kw):
~/Workspace/mars/mars/core.py in new_entities(self, inputs, shape, **kwargs)
578 entities = self._new_entities(inputs, shape, **kwargs)
579 if is_eager_mode():
--> 580 ExecutableTuple(entities).execute(fetch=False)
581 return entities
582
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
594 if session is None:
595 session = Session.default_or_local()
--> 596 return session.run(*self, **kw)
~/Workspace/mars/mars/session.py in run(self, *tensors, **kw)
107
108 tensors = tuple(mt.tensor(t) for t in tensors)
--> 109 result = self._sess.run(*tensors, **kw)
110
111 for t in tensors:
~/Workspace/mars/mars/session.py in run(self, *tensors, **kw)
49 if 'n_parallel' not in kw:
50 kw['n_parallel'] = cpu_count()
---> 51 res = self._executor.execute_tensors(tensors, **kw)
52 return res
53
~/Workspace/mars/mars/utils.py in _wrapped(*args, **kwargs)
352 def _wrapped(*args, **kwargs):
353 _kernel_mode.eager = False
--> 354 return_value = func(*args, **kwargs)
355 _kernel_mode.eager = None
356 return return_value
~/Workspace/mars/mars/executor.py in execute_tensors(self, tensors, fetch, n_parallel, n_thread, print_progress, mock, sparse_mock_percent)
451 concat_keys = []
452 for tensor in tensors:
--> 453 tensor.tiles()
454 chunk_keys = [c.key for c in tensor.chunks]
455 result_keys.extend(chunk_keys)
~/Workspace/mars/mars/tensor/core.py in tiles(self)
223
224 def tiles(self):
--> 225 return handler.tiles(self)
226
227 def single_tiles(self):
~/Workspace/mars/mars/tiles.py in tiles(self, tiles_obj)
172 if not preds or accessible:
173 if node.is_coarse() and node.op:
--> 174 tiled = self._dispatch(node.op)
175 self._assign_to([t.data for t in tiled], node.op.outputs)
176 visited.add(node)
~/Workspace/mars/mars/utils.py in _wrapped(*args, **kwargs)
352 def _wrapped(*args, **kwargs):
353 _kernel_mode.eager = False
--> 354 return_value = func(*args, **kwargs)
355 _kernel_mode.eager = None
356 return return_value
~/Workspace/mars/mars/tiles.py in _dispatch(self, op)
114 if hasattr(op_cls, 'tile'):
115 # has tile implementation
--> 116 return op_cls.tile(op)
117 for op_clz in self._handlers.keys():
118 if issubclass(op_cls, op_clz):
~/Workspace/mars/mars/tensor/expressions/arithmetic/core.py in tile(cls, op)
43 for out_index in itertools.product(*(map(range, out_chunk_shape))):
44 in_chunks = [t.cix[get_index(out_index[-t.ndim:], t)] if t.ndim != 0 else t.chunks[0]
---> 45 for t in inputs]
46 chunk_op = op.copy().reset_key()
47 chunk_shape = broadcast_shape(*(c.shape for c in in_chunks))
~/Workspace/mars/mars/tensor/expressions/arithmetic/core.py in <listcomp>(.0)
43 for out_index in itertools.product(*(map(range, out_chunk_shape))):
44 in_chunks = [t.cix[get_index(out_index[-t.ndim:], t)] if t.ndim != 0 else t.chunks[0]
---> 45 for t in inputs]
46 chunk_op = op.copy().reset_key()
47 chunk_shape = broadcast_shape(*(c.shape for c in in_chunks))
~/Workspace/mars/mars/core.py in __getitem__(self, item)
457 if len(item) != self._tilesable.ndim:
458 raise ValueError('Cannot get tensor chunk by %s, expect length %d' % (
--> 459 item, self._tilesable.ndim))
460
461 s = self._tilesable.chunk_shape
ValueError: Cannot get tensor chunk by (0, 0), expect length 3
In [7]: a[:, mt.newaxis, :] - a[mt.newaxis, ...]
Out[7]: Tensor <op=TensorSubtract, shape=(2, 2, 3), key=fe6279614688ba864daa99368caf12af>
```
| [
{
"content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Copyright 1999-2018 Alibaba Group Holding Ltd.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://... | [
{
"content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Copyright 1999-2018 Alibaba Group Holding Ltd.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://... | diff --git a/mars/tests/test_eager_mode.py b/mars/tests/test_eager_mode.py
index e58110e375..86d0bed93a 100644
--- a/mars/tests/test_eager_mode.py
+++ b/mars/tests/test_eager_mode.py
@@ -172,3 +172,23 @@ def testRepr(self):
self.assertNotIn(repr(np.ones((10, 10))), repr(a))
self.assertNotIn(str(np.ones((10, 10))), str(a))
+
+ def testRuntimeError(self):
+ from mars.utils import kernel_mode
+
+ @kernel_mode
+ def raise_error(*_):
+ raise ValueError
+
+ with option_context({'eager_mode': True}):
+ a = mt.zeros((10, 10))
+ with self.assertRaises(ValueError):
+ raise_error(a)
+
+ r = a + 1
+ self.assertIn(repr(np.zeros((10, 10)) + 1), repr(r))
+ np.testing.assert_array_equal(r.fetch(), np.zeros((10, 10)) + 1)
+
+ a = mt.zeros((10, 10))
+ with self.assertRaises(ValueError):
+ a.fetch()
diff --git a/mars/utils.py b/mars/utils.py
index 8efdde9880..f55ae291ea 100644
--- a/mars/utils.py
+++ b/mars/utils.py
@@ -350,9 +350,10 @@ def kernel_mode(func):
"""
def _wrapped(*args, **kwargs):
- _kernel_mode.eager = False
- return_value = func(*args, **kwargs)
- _kernel_mode.eager = None
- return return_value
+ try:
+ _kernel_mode.eager = False
+ return func(*args, **kwargs)
+ finally:
+ _kernel_mode.eager = None
return _wrapped
|
qutebrowser__qutebrowser-3318 | edit-command --run should clear the status bar
Thanks to @rcorre for implementing `:edit-command` from #2453. Quick issue: when using the `--run` flag, not only should the command be executed, but the status bar should also be cleared (or whatever `<esc>` tends to do). Here's what happens currently (v1.0.3, abb5c9f63):
- bind `<ctrl-e>` to `edit-command --run`
- do `:open<ctrl-e>`
- type `www.qutebrowser.org`
- save & quit
What happens:
1. get sent to the Qutebrowser home page
2. status bar still says `:open www.qutebrowser.org`, and I also see URLs from my history
What I expected to happen: 1 but not 2.
This means I need to hit `<esc>` after doing an `:edit-command --run`.
| [
{
"content": "# vim: ft=python fileencoding=utf-8 sts=4 sw=4 et:\n\n# Copyright 2014-2017 Florian Bruhin (The Compiler) <mail@qutebrowser.org>\n#\n# This file is part of qutebrowser.\n#\n# qutebrowser is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License... | [
{
"content": "# vim: ft=python fileencoding=utf-8 sts=4 sw=4 et:\n\n# Copyright 2014-2017 Florian Bruhin (The Compiler) <mail@qutebrowser.org>\n#\n# This file is part of qutebrowser.\n#\n# qutebrowser is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License... | diff --git a/qutebrowser/mainwindow/statusbar/command.py b/qutebrowser/mainwindow/statusbar/command.py
index a61f3c93d6b..bba2559ed92 100644
--- a/qutebrowser/mainwindow/statusbar/command.py
+++ b/qutebrowser/mainwindow/statusbar/command.py
@@ -178,7 +178,7 @@ def edit_command(self, run=False):
def callback(text):
self.set_cmd_text(text)
if run:
- self.got_cmd[str].emit(text)
+ self.command_accept()
ed.editing_finished.connect(callback)
ed.edit(self.text())
diff --git a/tests/end2end/features/editor.feature b/tests/end2end/features/editor.feature
index 21df30b4d00..79f4f17d43f 100644
--- a/tests/end2end/features/editor.feature
+++ b/tests/end2end/features/editor.feature
@@ -149,3 +149,4 @@ Feature: Opening external editors
And I set up a fake editor replacing "foo" by "bar"
And I run :edit-command --run
Then the message "bar" should be shown
+ And "Leaving mode KeyMode.command (reason: cmd accept)" should be logged
|
obspy__obspy-516 | Dead link in obspy.signal.cross_correlation.xcorr docstring
obspy.signal.cross_correlation.xcorr refers to ticket #249 in its docstring (see here: http://docs.obspy.org/packages/autogen/obspy.signal.cross_correlation.xcorr.html)
This is an old trac link. Either it should be updated to https://github.com/obspy/obspy/issues/249 or, better, the relevant parts of this ticket discussion should be included in the docstring.
What do you think?
| [
{
"content": "#!/usr/bin/env python\n#--------------------------------------------------------------------\n# Filename: cross_correlation.py\n# Author: Moritz Beyreuther, Tobias Megies\n# Email: megies@geophysik.uni-muenchen.de\n#\n# Copyright (C) 2008-2012 Moritz Beyreuther, Tobias Megies\n#--------------... | [
{
"content": "#!/usr/bin/env python\n#--------------------------------------------------------------------\n# Filename: cross_correlation.py\n# Author: Moritz Beyreuther, Tobias Megies\n# Email: megies@geophysik.uni-muenchen.de\n#\n# Copyright (C) 2008-2012 Moritz Beyreuther, Tobias Megies\n#--------------... | diff --git a/obspy/signal/cross_correlation.py b/obspy/signal/cross_correlation.py
index 35d81927abb..18c947bf813 100644
--- a/obspy/signal/cross_correlation.py
+++ b/obspy/signal/cross_correlation.py
@@ -65,7 +65,7 @@ def xcorr(tr1, tr2, shift_len, full_xcorr=False):
`ObsPy-users mailing list
<http://lists.obspy.org/pipermail/obspy-users/2011-March/000056.html>`_
and
- `ticket #249 <https://obspy.org/ticket/249>`_.
+ `issue #249 <https://github.com/obspy/obspy/issues/249>`_.
.. rubric:: Example
|
PlasmaPy__PlasmaPy-2304 | Unpin version of Sphinx after next release of sphinx-notfound-page
```py3tb
Exception occurred:
File "/home/runner/work/PlasmaPy/PlasmaPy/.tox/build_docs_pins/lib/python3.11/site-packages/notfound/extension.py", line 337, in setup
from sphinx.builders.html import setup_js_tag_helper
ImportError: cannot import name 'setup_js_tag_helper' from 'sphinx.builders.html' (/home/runner/work/PlasmaPy/PlasmaPy/.tox/build_docs_pins/lib/python3.11/site-packages/sphinx/builders/html/__init__.py)
The full traceback has been saved in /tmp/sphinx-err-a746pmo7.log, if you want to report the issue to the developers.
Please also report this if it was a user error, so that a better error message can be provided next time.
A bug report can be filed in the tracker at <https://github.com/sphinx-doc/sphinx/issues>. Thanks!
build_docs_pins: exit 2 (3.82 seconds) /home/runner/work/PlasmaPy/PlasmaPy> sphinx-build docs docs/_build/html -W -n --keep-going -b html -q pid=2209
.pkg: _exit> python /opt/hostedtoolcache/Python/3.11.4/x64/lib/python3.11/site-packages/pyproject_api/_backend.py True setuptools.build_meta
build_docs_pins: FAIL code 2 (81.97=setup[78.16]+cmd[3.82] seconds)
evaluation failed :( (82.05 seconds)
```
| [
{
"content": "\"\"\"The configuration file for building PlasmaPy's documentation.\"\"\"\n\n#!/usr/bin/env python3\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.a... | [
{
"content": "\"\"\"The configuration file for building PlasmaPy's documentation.\"\"\"\n\n#!/usr/bin/env python3\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.a... | diff --git a/docs/conf.py b/docs/conf.py
index 2493075f93..f4c21a7c41 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -416,6 +416,8 @@
(python_role, "automod.*"),
(python_role, "Builder"),
(python_role, "docutils.*"),
+ (python_role, "Documenter"),
+ (python_role, "Node"),
(python_role, "level"),
(python_role, ".*member.*"),
(python_role, "OptionSpec"),
diff --git a/pyproject.toml b/pyproject.toml
index 44487ddd92..79518dead8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -64,14 +64,14 @@ docs = [
"numpydoc >= 1.5.0",
"pillow >= 9.5.0",
"pygments >= 2.15.0",
- "sphinx == 6.2.1",
+ "sphinx >= 7.2.3",
"sphinx-changelog >= 1.3.0",
"sphinx-codeautolink >= 0.15.0",
"sphinx-copybutton >= 0.5.1",
"sphinx-gallery >= 0.12.2",
"sphinx-hoverxref >= 1.1.1",
"sphinx-issues >= 3.0.1",
- "sphinx-notfound-page >= 0.8.3",
+ "sphinx-notfound-page == 1.0.0rc1",
"sphinx-reredirects >= 0.1.1",
"sphinx_rtd_theme >= 1.2.2",
"sphinx_tabs >= 3.4.1",
diff --git a/requirements.txt b/requirements.txt
index 1791e5a3a0..b4ce56a085 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -391,7 +391,7 @@ sortedcontainers==2.4.0
# via hypothesis
soupsieve==2.4.1
# via beautifulsoup4
-sphinx==6.2.1
+sphinx==7.2.4
# via
# nbsphinx
# numpydoc
@@ -426,7 +426,7 @@ sphinx-hoverxref==1.3.0
# via plasmapy (setup.py)
sphinx-issues==3.0.1
# via plasmapy (setup.py)
-sphinx-notfound-page==0.8.3
+sphinx-notfound-page==1.0.0rc1
# via plasmapy (setup.py)
sphinx-reredirects==0.1.2
# via plasmapy (setup.py)
|
Bitmessage__PyBitmessage-1697 | Fix backward compatibility in pickle_deserialize_old_knownnodes()
Hello!
#1662 is caused by changed package structure.
Here I've set up a minimal upgrade from v0.6.3 to reproduce the bug. Using v0.6.2 would be difficult, because it has no command line args.
| [
{
"content": "\"\"\"\nManipulations with knownNodes dictionary.\n\"\"\"\n\nimport json\nimport logging\nimport os\nimport pickle\nimport threading\nimport time\ntry:\n from collections.abc import Iterable\nexcept ImportError:\n from collections import Iterable\n\nimport state\nfrom bmconfigparser import B... | [
{
"content": "\"\"\"\nManipulations with knownNodes dictionary.\n\"\"\"\n\nimport json\nimport logging\nimport os\nimport pickle\nimport threading\nimport time\ntry:\n from collections.abc import Iterable\nexcept ImportError:\n from collections import Iterable\n\nimport state\nfrom bmconfigparser import B... | diff --git a/src/network/knownnodes.py b/src/network/knownnodes.py
index 07871c7c7a..c92f8e9a3f 100644
--- a/src/network/knownnodes.py
+++ b/src/network/knownnodes.py
@@ -17,6 +17,8 @@
from bmconfigparser import BMConfigParser
from network.node import Peer
+state.Peer = Peer
+
knownNodesLock = threading.RLock()
"""Thread lock for knownnodes modification"""
knownNodes = {stream: {} for stream in range(1, 4)}
diff --git a/src/tests/core.py b/src/tests/core.py
index 3d8ac98382..03e8b948f0 100644
--- a/src/tests/core.py
+++ b/src/tests/core.py
@@ -7,6 +7,7 @@
import pickle # nosec
import Queue
import random # nosec
+import shutil
import string
import sys
import time
@@ -231,7 +232,8 @@ def test_version(self):
msg = protocol.assembleVersionMessage('127.0.0.1', 8444, [1])
decoded = self._decode_msg(msg, "IQQiiQlsLv")
peer, _, ua, streams = self._decode_msg(msg, "IQQiiQlsLv")[4:]
- self.assertEqual(peer, Node(3, '127.0.0.1', 8444))
+ self.assertEqual(
+ peer, Node(11 if state.dandelion else 3, '127.0.0.1', 8444))
self.assertEqual(ua, '/PyBitmessage:' + softwareVersion + '/')
self.assertEqual(streams, [1])
# with multiple streams
@@ -259,6 +261,19 @@ def test_insert_method_msgid(self):
'''select typeof(msgid) from sent where ackdata=?''', result)
self.assertEqual(column_type[0][0] if column_type else '', 'text')
+ def test_old_knownnodes_pickle(self):
+ """Testing old(v.0.6.2) version knownnodes.dat file"""
+ try:
+ old_source_file = os.path.join(
+ os.path.abspath(os.path.dirname(__file__)), 'test_pattern', 'knownnodes.dat')
+ new_destination_file = os.path.join(state.appdata, 'knownnodes.dat')
+ shutil.copyfile(old_source_file, new_destination_file)
+ knownnodes.readKnownNodes()
+ except AttributeError as e:
+ self.fail('Failed to load knownnodes: %s' % e)
+ finally:
+ cleanup(files=('knownnodes.dat',))
+
def run():
"""Starts all tests defined in this module"""
diff --git a/src/tests/test_pattern/knownnodes.dat b/src/tests/test_pattern/knownnodes.dat
new file mode 100644
index 0000000000..a78a4434b5
--- /dev/null
+++ b/src/tests/test_pattern/knownnodes.dat
@@ -0,0 +1,104 @@
+(dp0
+I1
+(dp1
+ccopy_reg
+_reconstructor
+p2
+(cstate
+Peer
+p3
+c__builtin__
+tuple
+p4
+(S'85.180.139.241'
+p5
+I8444
+tp6
+tp7
+Rp8
+I1608398841
+sg2
+(g3
+g4
+(S'158.222.211.81'
+p9
+I8080
+tp10
+tp11
+Rp12
+I1608398841
+sg2
+(g3
+g4
+(S'178.62.12.187'
+p13
+I8448
+tp14
+tp15
+Rp16
+I1608398841
+sg2
+(g3
+g4
+(S'109.147.204.113'
+p17
+I1195
+tp18
+tp19
+Rp20
+I1608398841
+sg2
+(g3
+g4
+(S'5.45.99.75'
+p21
+I8444
+tp22
+tp23
+Rp24
+I1608398841
+sg2
+(g3
+g4
+(S'178.11.46.221'
+p25
+I8444
+tp26
+tp27
+Rp28
+I1608398841
+sg2
+(g3
+g4
+(S'95.165.168.168'
+p29
+I8444
+tp30
+tp31
+Rp32
+I1608398841
+sg2
+(g3
+g4
+(S'24.188.198.204'
+p33
+I8111
+tp34
+tp35
+Rp36
+I1608398841
+sg2
+(g3
+g4
+(S'75.167.159.54'
+p37
+I8444
+tp38
+tp39
+Rp40
+I1608398841
+ssI2
+(dp41
+sI3
+(dp42
+s.
\ No newline at end of file
|
pyca__cryptography-4064 | Add a python_requires to our setup.py
cc: @dstufft
| [
{
"content": "#!/usr/bin/env python\n\n# This file is dual licensed under the terms of the Apache License, Version\n# 2.0, and the BSD License. See the LICENSE file in the root of this repository\n# for complete details.\n\nfrom __future__ import absolute_import, division, print_function\n\nimport os\nimport pl... | [
{
"content": "#!/usr/bin/env python\n\n# This file is dual licensed under the terms of the Apache License, Version\n# 2.0, and the BSD License. See the LICENSE file in the root of this repository\n# for complete details.\n\nfrom __future__ import absolute_import, division, print_function\n\nimport os\nimport pl... | diff --git a/setup.py b/setup.py
index b9186a8445be..9250e2da2ac7 100644
--- a/setup.py
+++ b/setup.py
@@ -283,6 +283,8 @@ def run_tests(self):
packages=find_packages(where="src", exclude=["_cffi_src", "_cffi_src.*"]),
include_package_data=True,
+ python_requires='>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*',
+
install_requires=[
"idna >= 2.1",
"asn1crypto >= 0.21.0",
|
networkx__networkx-4132 | edgelist in draw_network does not support arrays
Using numpy 1.18.2 and networkx 2.4, it is not possible to use a numpy array for the `edgelist` argument of [``draw_networkx``](https://networkx.github.io/documentation/stable/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html).
It raises:
```
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
I normally would not have opened an issue for that but I do not see the point of the current implementation, so in case it can be changed harmlessly to support numpy arrays, that might be a win-win.
The issue comes from lines 575-579 on 2.4 ([lines 640-647](https://github.com/networkx/networkx/blob/master/networkx/drawing/nx_pylab.py#L640) on master) that read:
```python
if edgelist is None:
edgelist = list(G.edges())
if not edgelist or len(edgelist) == 0: # no edges!
return None
```
I am not sure what the combination of ``not edgelist`` and ``len(edgelist) == 0`` is for, here, so I would propose to change it to
```python
if edgelist is None:
edgelist = list(G.edges())
elif len(edgelist) == 0: # no edges!
return None
```
which should still do the job for all valid inputs I can think of and would also work with numpy arrays.
**EDIT** if iterables should be supported, one could replace ``not edgelist`` by ``not isinstance(edgelist, Iterable)``
| [
{
"content": "\"\"\"\n**********\nMatplotlib\n**********\n\nDraw networks with matplotlib.\n\nSee Also\n--------\n\nmatplotlib: http://matplotlib.org/\n\npygraphviz: http://pygraphviz.github.io/\n\n\"\"\"\nfrom numbers import Number\nimport networkx as nx\nfrom networkx.drawing.layout import (\n shel... | [
{
"content": "\"\"\"\n**********\nMatplotlib\n**********\n\nDraw networks with matplotlib.\n\nSee Also\n--------\n\nmatplotlib: http://matplotlib.org/\n\npygraphviz: http://pygraphviz.github.io/\n\n\"\"\"\nfrom numbers import Number\nimport networkx as nx\nfrom networkx.drawing.layout import (\n shel... | diff --git a/networkx/drawing/nx_pylab.py b/networkx/drawing/nx_pylab.py
index eb7be33be8d..c705ed547b1 100644
--- a/networkx/drawing/nx_pylab.py
+++ b/networkx/drawing/nx_pylab.py
@@ -640,7 +640,7 @@ def draw_networkx_edges(
if edgelist is None:
edgelist = list(G.edges())
- if not edgelist or len(edgelist) == 0: # no edges!
+ if len(edgelist) == 0: # no edges!
if not G.is_directed() or not arrows:
return LineCollection(None)
else:
diff --git a/networkx/drawing/tests/test_pylab.py b/networkx/drawing/tests/test_pylab.py
index 7a07acd9c3c..3280a49ee12 100644
--- a/networkx/drawing/tests/test_pylab.py
+++ b/networkx/drawing/tests/test_pylab.py
@@ -250,3 +250,8 @@ def test_alpha_iter(self):
def test_error_invalid_kwds(self):
with pytest.raises(ValueError, match="Received invalid argument"):
nx.draw(self.G, foo="bar")
+
+ def test_np_edgelist(self):
+ # see issue #4129
+ np = pytest.importorskip("numpy")
+ nx.draw_networkx(self.G, edgelist=np.array([(0, 2), (0, 3)]))
|
Textualize__textual-4189 | SyntaxWarning for loading indicator widget
I receive this warning after upgrading to `0.52.0`:
```
/Users/cthompson/Library/Caches/pypoetry/virtualenvs/dolphie-z84eXs3q-py3.11/lib/python3.11/site-packages/textual/widgets/_loading_indicator.py:57: SyntaxWarning: "is" with a literal. Did you mean "=="?
if self.app.animation_level is "none":
```
https://github.com/Textualize/textual/blob/main/src/textual/widgets/_loading_indicator.py#L57
Seems we just need to change `is "none"` to `== "none"`
| [
{
"content": "from __future__ import annotations\n\nfrom time import time\n\nfrom rich.console import RenderableType\nfrom rich.style import Style\nfrom rich.text import Text\n\nfrom ..color import Gradient\nfrom ..events import Mount\nfrom ..widget import Widget\n\n\nclass LoadingIndicator(Widget):\n \"\"\"... | [
{
"content": "from __future__ import annotations\n\nfrom time import time\n\nfrom rich.console import RenderableType\nfrom rich.style import Style\nfrom rich.text import Text\n\nfrom ..color import Gradient\nfrom ..events import Mount\nfrom ..widget import Widget\n\n\nclass LoadingIndicator(Widget):\n \"\"\"... | diff --git a/CHANGELOG.md b/CHANGELOG.md
index 62ff923eb5..e591e95d44 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,6 +5,12 @@ All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](http://keepachangelog.com/)
and this project adheres to [Semantic Versioning](http://semver.org/).
+## Unreleased
+
+### Fixed
+
+- Fixed the check for animation level in `LoadingIndicator` https://github.com/Textualize/textual/issues/4188
+
## [0.52.0] - 2023-02-19
### Changed
diff --git a/src/textual/widgets/_loading_indicator.py b/src/textual/widgets/_loading_indicator.py
index a826c85f85..24bc3cf2de 100644
--- a/src/textual/widgets/_loading_indicator.py
+++ b/src/textual/widgets/_loading_indicator.py
@@ -54,7 +54,7 @@ def _on_mount(self, _: Mount) -> None:
self.auto_refresh = 1 / 16
def render(self) -> RenderableType:
- if self.app.animation_level is "none":
+ if self.app.animation_level == "none":
return Text("Loading...")
elapsed = time() - self._start_time
|
celery__celery-6741 | celery amqp repl broken in celery 5.0.3+
<!--
Please fill this template entirely and do not erase parts of it.
We reserve the right to close without a response
bug reports which are incomplete.
-->
# Checklist
<!--
To check an item on the list replace [ ] with [x].
-->
- [x] I have verified that the issue exists against the `master` branch of Celery.
- [x] This has already been asked to the [discussion group](https://groups.google.com/forum/#!forum/celery-users) first.
- [x] I have read the relevant section in the
[contribution guide](http://docs.celeryproject.org/en/latest/contributing.html#other-bugs)
on reporting bugs.
- [x] I have checked the [issues list](https://github.com/celery/celery/issues?q=is%3Aissue+label%3A%22Issue+Type%3A+Bug+Report%22+-label%3A%22Category%3A+Documentation%22)
for similar or identical bug reports.
- [x] I have checked the [pull requests list](https://github.com/celery/celery/pulls?q=is%3Apr+label%3A%22PR+Type%3A+Bugfix%22+-label%3A%22Category%3A+Documentation%22)
for existing proposed fixes.
- [x] I have checked the [commit log](https://github.com/celery/celery/commits/master)
to find out if the bug was already fixed in the master branch.
- [x] I have included all related issues and possible duplicate issues
in this issue (If there are none, check this box anyway).
## Mandatory Debugging Information
- [x] I have included the output of ``celery -A proj report`` in the issue.
(if you are not able to do this, then at least specify the Celery
version affected).
- [x] I have verified that the issue exists against the `master` branch of Celery.
- [x] I have included the contents of ``pip freeze`` in the issue.
- [x] I have included all the versions of all the external dependencies required
to reproduce this bug.
## Optional Debugging Information
<!--
Try some of the below if you think they are relevant.
It will help us figure out the scope of the bug and how many users it affects.
-->
- [ ] I have tried reproducing the issue on more than one Python version
and/or implementation.
- [ ] I have tried reproducing the issue on more than one message broker and/or
result backend.
- [ ] I have tried reproducing the issue on more than one version of the message
broker and/or result backend.
- [ ] I have tried reproducing the issue on more than one operating system.
- [ ] I have tried reproducing the issue on more than one workers pool.
- [ ] I have tried reproducing the issue with autoscaling, retries,
ETA/Countdown & rate limits disabled.
- [x] I have tried reproducing the issue after downgrading
and/or upgrading Celery and its dependencies.
## Related Issues and Possible Duplicates
<!--
Please make sure to search and mention any related issues
or possible duplicates to this issue as requested by the checklist above.
This may or may not include issues in other repositories that the Celery project
maintains or other repositories that are dependencies of Celery.
If you don't know how to mention issues, please refer to Github's documentation
on the subject: https://help.github.com/en/articles/autolinked-references-and-urls#issues-and-pull-requests
-->
#### Related Issues
- None
#### Possible Duplicates
- None
## Environment & Settings
<!-- Include the contents of celery --version below -->
**Celery version**:
<!-- Include the output of celery -A proj report below -->
<details>
<summary><b><code>celery report</code> Output:</b></summary>
<p>
```
software -> celery:5.0.5 (singularity) kombu:5.0.2 py:3.6.9
billiard:3.6.4.0 py-amqp:5.0.6
platform -> system:Linux arch:64bit, ELF
kernel version:4.15.0-140-generic imp:CPython
loader -> celery.loaders.default.Loader
settings -> transport:amqp results:mongodb+srv://md_app_user:**@md-mongo.privatecircle.co/master_docs
accept_content: ['json']
broker_url: 'amqp://md_app_user:********@****************:5672//master_docs'
default_timezone: 'Asia/Kolkata'
imports: ['tasks']
result_backend: 'mongodb+srv://md_app_user:********@******************/master_docs'
result_serializer: 'json'
task_serializer: 'json'
timezone: 'Asia/Kolkata'
deprecated_settings: None
```
</p>
</details>
# Steps to Reproduce
## Required Dependencies
<!-- Please fill the required dependencies to reproduce this issue -->
* **Minimal Python Version**: 3.6.9
* **Minimal Celery Version**: 5.0.3
* **Minimal Kombu Version**: 5.0.2
* **Minimal Broker Version**: RabbitMQ 3.8.11
* **Minimal Result Backend Version**: Mongo 4.4
* **Minimal OS and/or Kernel Version**: Ubuntu 18.04.5 (Linux kernel 4.15.0-140)
* **Minimal Broker Client Version**: N/A or Unknown
* **Minimal Result Backend Client Version**: N/A or Unknown
### Python Packages
<!-- Please fill the contents of pip freeze below -->
<details>
<summary><b><code>pip freeze</code> Output:</b></summary>
<p>
```
amqp==5.0.6
backcall==0.2.0
billiard==3.6.4.0
boto3==1.17.51
botocore==1.20.51
cached-property==1.5.2
cchardet==2.1.7
celery==5.0.5
certifi==2020.12.5
chardet==4.0.0
click==7.1.2
click-didyoumean==0.0.3
click-plugins==1.1.1
click-repl==0.1.6
decorator==5.0.6
dnspython==2.1.0
idna==2.10
importlib-metadata==3.10.0
ipython==7.16.1
ipython-genutils==0.2.0
jedi==0.17.2
jmespath==0.10.0
kombu==5.0.2
lxml==4.6.3
parso==0.7.1
pexpect==4.8.0
pickleshare==0.7.5
prompt-toolkit==3.0.18
ptyprocess==0.7.0
Pygments==2.8.1
pymongo==3.11.3
python-dateutil==2.8.1
python-magic==0.4.22
pytz==2021.1
requests==2.25.1
s3transfer==0.3.6
six==1.15.0
traitlets==4.3.3
typing-extensions==3.7.4.3
urllib3==1.26.4
vine==5.0.0
wcwidth==0.2.5
zipp==3.4.1
```
</p>
</details>
### Other Dependencies
<!--
Please provide system dependencies, configuration files
and other dependency information if applicable
-->
<details>
<p>
N/A
</p>
</details>
## Minimally Reproducible Test Case
<!--
Please provide a reproducible test case.
Refer to the Reporting Bugs section in our contribution guide.
We prefer submitting test cases in the form of a PR to our integration test suite.
If you can provide one, please mention the PR number below.
If not, please attach the most minimal code example required to reproduce the issue below.
If the test case is too large, please include a link to a gist or a repository below.
-->
<details>
<p>
```python
# test.py
import celery
app = celery.Celery('proj')
```
```shell
$ celery -A test amqp repl
> exchange.declare
```
</p>
</details>
# Expected Behavior
<!-- Describe in detail what you expect to happen -->
The AMQP interactive shell should accept this command and execute it and then prompt for the next command.
# Actual Behavior
<!--
Describe in detail what actually happened.
Please include a backtrace and surround it with triple backticks (```).
In addition, include the Celery daemon logs, the broker logs,
the result backend logs and system logs below if they will help us debug
the issue.
-->
```
Traceback (most recent call last):
File "/home/privatecircle/.virtualenvs/mca_document_manager/bin/celery", line 8, in <module>
sys.exit(main())
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/celery/__main__.py", line 15, in main
sys.exit(_main())
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/celery/bin/celery.py", line 213, in main
return celery(auto_envvar_prefix="CELERY")
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click/core.py", line 829, in __call__
return self.main(*args, **kwargs)
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click/core.py", line 782, in main
rv = self.invoke(ctx)
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click/core.py", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click/core.py", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click/core.py", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click/core.py", line 610, in invoke
return callback(*args, **kwargs)
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click/decorators.py", line 21, in new_func
return f(get_current_context(), *args, **kwargs)
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click_repl/__init__.py", line 248, in repl
group.invoke(ctx)
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click/core.py", line 1256, in invoke
Command.invoke(self, ctx)
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click/core.py", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click/core.py", line 610, in invoke
return callback(*args, **kwargs)
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click/decorators.py", line 21, in new_func
return f(get_current_context(), *args, **kwargs)
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/celery/bin/base.py", line 120, in caller
app = ctx.obj.app
AttributeError: 'AMQPContext' object has no attribute 'app'
```
| [
{
"content": "\"\"\"AMQP 0.9.1 REPL.\"\"\"\n\nimport pprint\n\nimport click\nfrom amqp import Connection, Message\nfrom click_repl import register_repl\n\n__all__ = ('amqp',)\n\nfrom celery.bin.base import handle_preload_options\n\n\ndef dump_message(message):\n if message is None:\n return 'No messag... | [
{
"content": "\"\"\"AMQP 0.9.1 REPL.\"\"\"\n\nimport pprint\n\nimport click\nfrom amqp import Connection, Message\nfrom click_repl import register_repl\n\n__all__ = ('amqp',)\n\nfrom celery.bin.base import handle_preload_options\n\n\ndef dump_message(message):\n if message is None:\n return 'No messag... | diff --git a/celery/bin/amqp.py b/celery/bin/amqp.py
index ab8ab5f0100..29c625281ed 100644
--- a/celery/bin/amqp.py
+++ b/celery/bin/amqp.py
@@ -25,6 +25,10 @@ def __init__(self, cli_context):
self.connection = self.cli_context.app.connection()
self.channel = None
self.reconnect()
+
+ @property
+ def app(self):
+ return self.cli_context.app
def respond(self, retval):
if isinstance(retval, str):
|
iterative__dvc-3428 | add: empty files add broken when cache mode is hardlinks
## DVC Version
```
DVC version: 0.86.5+f67314.mod
Python version: 3.7.6
Platform: Darwin-18.2.0-x86_64-i386-64bit
Binary: False
Package: None
Cache: reflink - supported, hardlink - supported, symlink - supported
Filesystem type (cache directory): ('apfs', '/dev/disk1s1')
Filesystem type (workspace): ('apfs', '/dev/disk1s1')
```
## Reproduce
```
dvc config cache.type hardlink
touch foo
dvc add foo
```
outputs:
```
ERROR: no possible cache types left to try out.
```
| [
{
"content": "import errno\nimport logging\nimport os\nimport stat\nfrom concurrent.futures import ThreadPoolExecutor\nfrom functools import partial\n\nfrom shortuuid import uuid\n\nfrom dvc.compat import fspath_py35\nfrom dvc.exceptions import DvcException, DownloadError, UploadError\nfrom dvc.path_info import... | [
{
"content": "import errno\nimport logging\nimport os\nimport stat\nfrom concurrent.futures import ThreadPoolExecutor\nfrom functools import partial\n\nfrom shortuuid import uuid\n\nfrom dvc.compat import fspath_py35\nfrom dvc.exceptions import DvcException, DownloadError, UploadError\nfrom dvc.path_info import... | diff --git a/dvc/remote/local.py b/dvc/remote/local.py
index 24725c526d..3e62e0628e 100644
--- a/dvc/remote/local.py
+++ b/dvc/remote/local.py
@@ -92,6 +92,12 @@ def already_cached(self, path_info):
return not self.changed_cache(current_md5)
+ def _verify_link(self, path_info, link_type):
+ if link_type == "hardlink" and self.getsize(path_info) == 0:
+ return
+
+ super()._verify_link(path_info, link_type)
+
def is_empty(self, path_info):
path = path_info.fspath
diff --git a/tests/func/test_add.py b/tests/func/test_add.py
index 95407ee7d0..9d966452dd 100644
--- a/tests/func/test_add.py
+++ b/tests/func/test_add.py
@@ -7,7 +7,7 @@
import colorama
import pytest
-from mock import patch
+from mock import patch, call
import dvc as dvc_module
from dvc.cache import Cache
@@ -662,3 +662,36 @@ def test_not_raises_on_re_add(tmp_dir, dvc):
tmp_dir.gen({"file2": "file2 content", "file": "modified file"})
dvc.add(["file2", "file"])
+
+
+@pytest.mark.parametrize("link", ["hardlink", "symlink", "copy"])
+def test_add_empty_files(tmp_dir, dvc, link):
+ file = "foo"
+ dvc.cache.local.cache_types = [link]
+ stages = tmp_dir.dvc_gen(file, "")
+
+ assert (tmp_dir / file).exists()
+ assert (tmp_dir / (file + Stage.STAGE_FILE_SUFFIX)).exists()
+ assert os.path.exists(stages[0].outs[0].cache_path)
+
+
+def test_add_optimization_for_hardlink_on_empty_files(tmp_dir, dvc, mocker):
+ dvc.cache.local.cache_types = ["hardlink"]
+ tmp_dir.gen({"foo": "", "bar": "", "lorem": "lorem", "ipsum": "ipsum"})
+ m = mocker.spy(RemoteLOCAL, "is_hardlink")
+ stages = dvc.add(["foo", "bar", "lorem", "ipsum"])
+
+ assert m.call_count == 1
+ assert m.call_args != call(tmp_dir / "foo")
+ assert m.call_args != call(tmp_dir / "bar")
+
+ for stage in stages[:2]:
+ # hardlinks are not created for empty files
+ assert not System.is_hardlink(stage.outs[0].path_info)
+
+ for stage in stages[2:]:
+ assert System.is_hardlink(stage.outs[0].path_info)
+
+ for stage in stages:
+ assert os.path.exists(stage.path)
+ assert os.path.exists(stage.outs[0].cache_path)
|
OpenNMT__OpenNMT-py-471 | tools/embeddings_to_torch.py fails when some word features are included in the preprocessing step
When there are some word features appended to each token in the source text, it seems that the `tools/embeddings_to_torch.py` script cannot extract correct vocabulary from the dataset.
```
$ python tools/embeddings_to_torch.py -emb_file /path/to/word.vectors.txt -dict_file dataset.vocab.pt -output dataset.emb
Traceback (most recent call last):
File "tools/embeddings_to_torch.py", line 94, in <module>
main()
File "tools/embeddings_to_torch.py", line 62, in main
enc_vocab, dec_vocab = get_vocabs(opt.dict_file)
File "tools/embeddings_to_torch.py", line 24, in get_vocabs
enc_vocab, dec_vocab = [vocab[1] for vocab in vocabs]
ValueError: too many values to unpack (expected 2)
```
| [
{
"content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom __future__ import print_function\nfrom __future__ import division\nimport six\nimport sys\nimport numpy as np\nimport argparse\nimport torch\n\nparser = argparse.ArgumentParser(description='embeddings_to_torch.py')\nparser.add_argument('-emb_fil... | [
{
"content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom __future__ import print_function\nfrom __future__ import division\nimport six\nimport sys\nimport numpy as np\nimport argparse\nimport torch\n\nparser = argparse.ArgumentParser(description='embeddings_to_torch.py')\nparser.add_argument('-emb_fil... | diff --git a/tools/embeddings_to_torch.py b/tools/embeddings_to_torch.py
index 7ec470ef51..4b4294930e 100755
--- a/tools/embeddings_to_torch.py
+++ b/tools/embeddings_to_torch.py
@@ -21,7 +21,7 @@
def get_vocabs(dict_file):
vocabs = torch.load(dict_file)
- enc_vocab, dec_vocab = [vocab[1] for vocab in vocabs]
+ enc_vocab, dec_vocab = vocabs[0][1], vocabs[-1][1]
print("From: %s" % dict_file)
print("\t* source vocab: %d words" % len(enc_vocab))
|
zestedesavoir__zds-site-6488 | Possible erreur 500 à la résolution d'une alerte sur un contenu qui n'est plus public
Rapporté par Sentry. J'ai eu du mal à comprendre comment le bug a pu se produire, mais j'ai réussi à le reproduire (d'une façon peut-être un peu tirée par les cheveux...).
**Comment reproduire ?**
1. Se connecter en tant que `user1`
2. Signaler un billet
3. Se connecter en tant que `staff`
4. Ouvrir la page du billet signalé dans deux onglets différents
5. Sur un des onglets, dépublier le billet
6. Sur l'autre onglet, résoudre l'alerte (ne pas recharger la page juste avant, le billet n'est en fait plus publié, c'est là qu'est l'astuce)
Une erreur 500 va alors apparaître. Elle provient d'ici : https://github.com/zestedesavoir/zds-site/blob/c06671c4901a95c30f31067c09d5e4526fd86575/zds/tutorialv2/views/alerts.py#L88
Le contenu n'a plus de version publique, donc plus d'URL publique, et `content.get_absolute_url_online()` renvoie alors `''`.
La correction de ce bug passe sans doute par la vérification si l'alerte est déjà résolue ou si le contenu signalé a bien une version publique : si l'une de ces conditions n'est pas remplie, une erreur 404 devrait être levée.
| [
{
"content": "from datetime import datetime\n\nfrom django.contrib import messages\nfrom django.core.exceptions import PermissionDenied\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.db import transaction\nfrom django.http import Http404\nfrom django.shortcuts import get_object_or_404, ... | [
{
"content": "from datetime import datetime\n\nfrom django.contrib import messages\nfrom django.core.exceptions import PermissionDenied\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.db import transaction\nfrom django.http import Http404\nfrom django.shortcuts import get_object_or_404, ... | diff --git a/zds/tutorialv2/tests/tests_utils.py b/zds/tutorialv2/tests/tests_utils.py
index 4e52de2902..c3bcc62e2d 100644
--- a/zds/tutorialv2/tests/tests_utils.py
+++ b/zds/tutorialv2/tests/tests_utils.py
@@ -556,7 +556,7 @@ def test_no_alert_on_unpublish(self):
reaction = ContentReactionFactory(
related_content=published, author=ProfileFactory().user, position=1, pubdate=datetime.datetime.now()
)
- Alert.objects.create(
+ alert = Alert.objects.create(
scope="CONTENT",
comment=reaction,
text="a text",
@@ -569,6 +569,15 @@ def test_no_alert_on_unpublish(self):
unpublish_content(published, staff)
self.assertEqual(0, get_header_notifications(staff)["alerts"]["total"])
+ # Try to solve the alert anyway (related to #6478):
+ self.client.force_login(self.staff)
+ result = self.client.post(
+ reverse("content:resolve-content", kwargs={"pk": published.pk}),
+ {"alert_pk": alert.pk, "text": "Anéfé!"},
+ follow=False,
+ )
+ self.assertEqual(result.status_code, 404)
+
def tearDown(self):
super().tearDown()
PublicatorRegistry.registry = self.old_registry
diff --git a/zds/tutorialv2/views/alerts.py b/zds/tutorialv2/views/alerts.py
index d120707b1e..880e592bfa 100644
--- a/zds/tutorialv2/views/alerts.py
+++ b/zds/tutorialv2/views/alerts.py
@@ -62,6 +62,9 @@ def post(self, request, *args, **kwargs):
except (KeyError, ValueError):
raise Http404("L'alerte n'existe pas.")
+ if alert.solved:
+ raise Http404("L'alerte a déjà été résolue.")
+
resolve_reason = ""
msg_title = ""
msg_content = ""
|
zestedesavoir__zds-site-2270 | Erreur 500 lors de la recherche d'un sujet
Url incriminée : `http://beta.zestedesavoir.com/forums/sujets/recherche/`
On ne devrait jamais avoir d'erreur 500
| [
{
"content": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\nfrom datetime import datetime\nimport json\n\nfrom django.conf import settings\nfrom django.db.models import Q\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.models import User\nf... | [
{
"content": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\nfrom datetime import datetime\nimport json\n\nfrom django.conf import settings\nfrom django.db.models import Q\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.models import User\nf... | diff --git a/zds/forum/views.py b/zds/forum/views.py
index e45bcc10ba..df8bf870db 100644
--- a/zds/forum/views.py
+++ b/zds/forum/views.py
@@ -1061,6 +1061,9 @@ def followed_topics(request):
def complete_topic(request):
+ if not request.GET.get('q', None):
+ return HttpResponse("{}", content_type='application/json')
+
sqs = SearchQuerySet().filter(content=AutoQuery(request.GET.get('q'))).order_by('-pubdate').all()
suggestions = {}
|
dbt-labs__dbt-core-7221 | [CT-1943] Loosen pin on `jsonschema` (via `hologram`)
For more context on our latest thinking around dependencies (how & why we pin today, and how we want it to change):
- https://github.com/dbt-labs/dbt-core/discussions/6495
### Summary
`dbt-core` depends on `hologram`, and as such it also includes `hologram`'s transitive dependencies on `jsonschema` and `python-dateutil`. `hologram`'s upper bound on `jsonschema` in particular is causing issues for some folks trying to install `dbt-core` alongside other popular tools, such as Airflow:
- https://github.com/dbt-labs/hologram/issues/52
- https://github.com/dbt-labs/hologram/pull/51
### Short term
- Try removing upper bound on `jsonschema`
- Release a new version of `hologram` with no / looser upper bound
- Support the new version of `hologram` [in `dbt-core`](https://github.com/dbt-labs/dbt-core/blob/a8abc496323f741d3218d298d5d2bb118fa01017/core/setup.py#L54)
### Medium term
Remove `dbt-core`'s dependency on `hologram` entirely. It doesn't do nearly as much for us today as it used to, and the validation errors it raises aren't even all that nice.
- https://github.com/dbt-labs/dbt-core/issues/6776
| [
{
"content": "#!/usr/bin/env python\nimport os\nimport sys\n\nif sys.version_info < (3, 7, 2):\n print(\"Error: dbt does not support this version of Python.\")\n print(\"Please upgrade to Python 3.7.2 or higher.\")\n sys.exit(1)\n\n\nfrom setuptools import setup\n\ntry:\n from setuptools import find... | [
{
"content": "#!/usr/bin/env python\nimport os\nimport sys\n\nif sys.version_info < (3, 7, 2):\n print(\"Error: dbt does not support this version of Python.\")\n print(\"Please upgrade to Python 3.7.2 or higher.\")\n sys.exit(1)\n\n\nfrom setuptools import setup\n\ntry:\n from setuptools import find... | diff --git a/.changes/unreleased/Under the Hood-20230324-144050.yaml b/.changes/unreleased/Under the Hood-20230324-144050.yaml
new file mode 100644
index 00000000000..9094cf7524b
--- /dev/null
+++ b/.changes/unreleased/Under the Hood-20230324-144050.yaml
@@ -0,0 +1,6 @@
+kind: Under the Hood
+body: Remove upper pin for hologram/jsonschema
+time: 2023-03-24T14:40:50.574108-04:00
+custom:
+ Author: gshank
+ Issue: "6775"
diff --git a/core/setup.py b/core/setup.py
index 9fe0368477f..e9d2eea37ef 100644
--- a/core/setup.py
+++ b/core/setup.py
@@ -50,7 +50,7 @@
"agate>=1.6,<1.7.1",
"click>=7.0,<9",
"colorama>=0.3.9,<0.4.7",
- "hologram>=0.0.14,<=0.0.15",
+ "hologram>=0.0.14,<=0.0.16",
"isodate>=0.6,<0.7",
"logbook>=1.5,<1.6",
"mashumaro[msgpack]==3.3.1",
|
streamlink__streamlink-2171 | INE Plugin
## Plugin Issue
<!-- Replace [ ] with [x] in order to check the box -->
- [X] This is a plugin issue and I have read the contribution guidelines.
### Description
The INE plugin doesn't appear to work on any videos I try.
### Reproduction steps / Explicit stream URLs to test
Try do download a video
### Log output
<!--
TEXT LOG OUTPUT IS REQUIRED for a plugin issue!
Use the `--loglevel debug` parameter and avoid using parameters which suppress log output.
https://streamlink.github.io/cli.html#cmdoption-l
Make sure to **remove usernames and passwords**
You can copy the output to https://gist.github.com/ or paste it below.
-->
```
streamlink https://streaming.ine.com/play/419cdc1a-a4a8-4eba-b8b3-5dda324daa94/day-1-part-1#/ --http-cookie laravel_session=<Removed> --loglevel debug
[cli][debug] OS: macOS 10.14.1
[cli][debug] Python: 2.7.10
[cli][debug] Streamlink: 0.14.2
[cli][debug] Requests(2.19.1), Socks(1.6.7), Websocket(0.54.0)
[cli][info] Found matching plugin ine for URL https://streaming.ine.com/play/419cdc1a-a4a8-4eba-b8b3-5dda324daa94/day-1-part-1#/
[plugin.ine][debug] Found video ID: 419cdc1a-a4a8-4eba-b8b3-5dda324daa94
[plugin.ine][debug] Loading player JS: https://content.jwplatform.com/players/yyYIR4k9-p4NBeNN0.js?exp=1543579899&sig=5e0058876669be2e2aafc7e52d067b78
error: Unable to validate result: <_sre.SRE_Match object at 0x106564dc8> does not equal None or Unable to validate key 'playlist': Type of u'//content.jwplatform.com/v2/media/yyYIR4k9?token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJyZWNvbW1lbmRhdGlvbnNfcGxheWxpc3RfaWQiOiJ5cHQwdDR4aCIsInJlc291cmNlIjoiL3YyL21lZGlhL3l5WUlSNGs5IiwiZXhwIjoxNTQzNTc5OTIwfQ.pHEgoDYzc219-S_slfWRhyEoCsyCZt74BiL8RNs5IJ8' should be 'str' but is 'unicode'
```
### Additional comments, screenshots, etc.
[Love Streamlink? Please consider supporting our collective. Thanks!](https://opencollective.com/streamlink/donate)
INE Plugin
## Plugin Issue
<!-- Replace [ ] with [x] in order to check the box -->
- [X] This is a plugin issue and I have read the contribution guidelines.
### Description
The INE plugin doesn't appear to work on any videos I try.
### Reproduction steps / Explicit stream URLs to test
Try do download a video
### Log output
<!--
TEXT LOG OUTPUT IS REQUIRED for a plugin issue!
Use the `--loglevel debug` parameter and avoid using parameters which suppress log output.
https://streamlink.github.io/cli.html#cmdoption-l
Make sure to **remove usernames and passwords**
You can copy the output to https://gist.github.com/ or paste it below.
-->
```
streamlink https://streaming.ine.com/play/419cdc1a-a4a8-4eba-b8b3-5dda324daa94/day-1-part-1#/ --http-cookie laravel_session=<Removed> --loglevel debug
[cli][debug] OS: macOS 10.14.1
[cli][debug] Python: 2.7.10
[cli][debug] Streamlink: 0.14.2
[cli][debug] Requests(2.19.1), Socks(1.6.7), Websocket(0.54.0)
[cli][info] Found matching plugin ine for URL https://streaming.ine.com/play/419cdc1a-a4a8-4eba-b8b3-5dda324daa94/day-1-part-1#/
[plugin.ine][debug] Found video ID: 419cdc1a-a4a8-4eba-b8b3-5dda324daa94
[plugin.ine][debug] Loading player JS: https://content.jwplatform.com/players/yyYIR4k9-p4NBeNN0.js?exp=1543579899&sig=5e0058876669be2e2aafc7e52d067b78
error: Unable to validate result: <_sre.SRE_Match object at 0x106564dc8> does not equal None or Unable to validate key 'playlist': Type of u'//content.jwplatform.com/v2/media/yyYIR4k9?token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJyZWNvbW1lbmRhdGlvbnNfcGxheWxpc3RfaWQiOiJ5cHQwdDR4aCIsInJlc291cmNlIjoiL3YyL21lZGlhL3l5WUlSNGs5IiwiZXhwIjoxNTQzNTc5OTIwfQ.pHEgoDYzc219-S_slfWRhyEoCsyCZt74BiL8RNs5IJ8' should be 'str' but is 'unicode'
```
### Additional comments, screenshots, etc.
[Love Streamlink? Please consider supporting our collective. Thanks!](https://opencollective.com/streamlink/donate)
| [
{
"content": "from __future__ import print_function\n\nimport json\nimport re\n\nfrom streamlink.plugin import Plugin\nfrom streamlink.plugin.api import validate\nfrom streamlink.stream import HLSStream, HTTPStream\nfrom streamlink.utils import update_scheme\n\n\nclass INE(Plugin):\n url_re = re.compile(r\"\... | [
{
"content": "from __future__ import print_function\n\nimport json\nimport re\n\nfrom streamlink.plugin import Plugin\nfrom streamlink.plugin.api import validate\nfrom streamlink.stream import HLSStream, HTTPStream\nfrom streamlink.utils import update_scheme\n\n\nclass INE(Plugin):\n url_re = re.compile(r\"\... | diff --git a/src/streamlink/plugins/ine.py b/src/streamlink/plugins/ine.py
index 8ebc91095f5..e0389b7e937 100644
--- a/src/streamlink/plugins/ine.py
+++ b/src/streamlink/plugins/ine.py
@@ -23,7 +23,7 @@ class INE(Plugin):
validate.all(
validate.get(1),
validate.transform(json.loads),
- {"playlist": str},
+ {"playlist": validate.text},
validate.get("playlist")
)
)
|
praw-dev__praw-1441 | PRAW 6.5.1 and 7.0.0 require Python versions above 3.5.2
**Describe the bug**
At https://praw.readthedocs.io/en/latest/getting_started/installation.html, it says:
> PRAW supports Python 3.5+
3.5.2 seems to be insufficient for PRAW versions after 6.4.0. I *think* 3.5.3 is probably sufficient based on what I have read searching for information on this error message, but I am skipping that version on this particular system so I haven't confirmed.
**To Reproduce**
Steps to reproduce the behavior:
1. Upgrade PRAW to either version 6.5.1 or 7.0.0
2. Run a simple PRAW script
3. Get this error:
```
$ python3 ~/test.py
Traceback (most recent call last):
File "/home/myusername/test.py", line 5, in <module>
import praw
File "/home/myusername/.local/lib/python3.5/site-packages/praw/__init__.py", line 14, in <module>
from .reddit import Reddit # NOQA
File "/home/myusername/.local/lib/python3.5/site-packages/praw/reddit.py", line 50, in <module>
class Reddit:
File "/home/myusername/.local/lib/python3.5/site-packages/praw/reddit.py", line 128, in Reddit
requestor_kwargs: Dict[str, Any] = None,
File "/usr/lib/python3.5/typing.py", line 649, in __getitem__
return Union[arg, type(None)]
File "/usr/lib/python3.5/typing.py", line 552, in __getitem__
dict(self.__dict__), parameters, _root=True)
File "/usr/lib/python3.5/typing.py", line 512, in __new__
for t2 in all_params - {t1} if not isinstance(t2, TypeVar)):
File "/usr/lib/python3.5/typing.py", line 512, in <genexpr>
for t2 in all_params - {t1} if not isinstance(t2, TypeVar)):
File "/usr/lib/python3.5/typing.py", line 1077, in __subclasscheck__
if super().__subclasscheck__(cls):
File "/usr/lib/python3.5/abc.py", line 225, in __subclasscheck__
for scls in cls.__subclasses__():
TypeError: descriptor '__subclasses__' of 'type' object needs an argument
```
**Expected behavior**
Python 3.5.2 works fine with PRAW 6.4.0 and earlier.
**Code/Logs**
`import praw` will do the trick.
**System Info**
- OS: Linux
- Python: 3.5.2
- PRAW Version: 6.5.1 or 7.0.0
| [
{
"content": "\"\"\"praw setup.py\"\"\"\n\nimport re\nfrom codecs import open\nfrom os import path\n\nfrom setuptools import find_packages, setup\n\nPACKAGE_NAME = \"praw\"\nHERE = path.abspath(path.dirname(__file__))\nwith open(path.join(HERE, \"README.rst\"), encoding=\"utf-8\") as fp:\n README = fp.read()... | [
{
"content": "\"\"\"praw setup.py\"\"\"\n\nimport re\nfrom codecs import open\nfrom os import path\n\nfrom setuptools import find_packages, setup\n\nPACKAGE_NAME = \"praw\"\nHERE = path.abspath(path.dirname(__file__))\nwith open(path.join(HERE, \"README.rst\"), encoding=\"utf-8\") as fp:\n README = fp.read()... | diff --git a/setup.py b/setup.py
index 794394d50..2ea8a6914 100644
--- a/setup.py
+++ b/setup.py
@@ -35,7 +35,7 @@
name=PACKAGE_NAME,
author="Bryce Boe",
author_email="bbzbryce@gmail.com",
- python_requires=">=3.5",
+ python_requires=">3.5.3",
classifiers=[
"Development Status :: 5 - Production/Stable",
"Environment :: Console",
|
svthalia__concrexit-1880 | ImproperlyConfigured: Field name `language` is not valid for model `Profile`.
Sentry Issue: [CONCREXIT-8J](https://sentry.io/organizations/thalia/issues/2580014551/?referrer=github_integration)
```
ImproperlyConfigured: Field name `language` is not valid for model `Profile`.
(14 additional frame(s) were not displayed)
...
File "django/utils/functional.py", line 48, in __get__
res = instance.__dict__[self.name] = self.func(instance)
File "rest_framework/serializers.py", line 349, in fields
for key, value in self.get_fields().items():
File "rest_framework/serializers.py", line 1053, in get_fields
field_class, field_kwargs = self.build_field(
File "rest_framework/serializers.py", line 1199, in build_field
return self.build_unknown_field(field_name, model_class)
File "rest_framework/serializers.py", line 1317, in build_unknown_field
raise ImproperlyConfigured(
```
| [
{
"content": "\"\"\"DRF serializers defined by the members package.\"\"\"\nfrom django.templatetags.static import static\nfrom rest_framework import serializers\n\nfrom members.models import Member, Profile\nfrom members.services import member_achievements, member_societies\nfrom thaliawebsite.api.services impo... | [
{
"content": "\"\"\"DRF serializers defined by the members package.\"\"\"\nfrom django.templatetags.static import static\nfrom rest_framework import serializers\n\nfrom members.models import Member, Profile\nfrom members.services import member_achievements, member_societies\nfrom thaliawebsite.api.services impo... | diff --git a/website/members/api/v1/serializers.py b/website/members/api/v1/serializers.py
index 008f4e6de..42d142327 100644
--- a/website/members/api/v1/serializers.py
+++ b/website/members/api/v1/serializers.py
@@ -125,7 +125,6 @@ class Meta:
"profile_description",
"nickname",
"display_name_preference",
- "language",
"receive_optin",
"receive_newsletter",
"receive_magazine",
|
joke2k__faker-826 | pt_BR email not returning valid email addresses
When creating a fake Factory with the pt_BR it is not returning valid email addresses.
Example:
```
melocauã@bol.com.br
joão-gabrielferreira@ig.com.br
lavíniarodrigues@sales.org
vitória78@example.br
```
| [
{
"content": "# coding=utf-8\nfrom __future__ import unicode_literals\nfrom .. import Provider as InternetProvider\n\n\nclass Provider(InternetProvider):\n safe_email_tlds = ('com', 'net', 'br', 'br')\n free_email_domains = (\n 'gmail.com',\n 'hotmail.com',\n 'yahoo.com.br',\n ... | [
{
"content": "# coding=utf-8\nfrom __future__ import unicode_literals\nfrom .. import Provider as InternetProvider\n\n\nclass Provider(InternetProvider):\n safe_email_tlds = ('com', 'net', 'br', 'br')\n free_email_domains = (\n 'gmail.com',\n 'hotmail.com',\n 'yahoo.com.br',\n ... | diff --git a/faker/providers/internet/pt_BR/__init__.py b/faker/providers/internet/pt_BR/__init__.py
index bf2b95f422..36d0e42400 100644
--- a/faker/providers/internet/pt_BR/__init__.py
+++ b/faker/providers/internet/pt_BR/__init__.py
@@ -13,3 +13,11 @@ class Provider(InternetProvider):
'bol.com.br',
'ig.com.br')
tlds = ('com', 'com', 'com', 'net', 'org', 'br', 'br', 'br')
+ replacements = (
+ ('à', 'a'), ('â', 'a'), ('ã', 'a'),
+ ('ç', 'c'),
+ ('é', 'e'), ('ê', 'e'),
+ ('í', 'i'),
+ ('ô', 'o'), ('ö', 'o'), ('õ', 'o'),
+ ('ú', 'u'),
+ )
diff --git a/tests/providers/test_internet.py b/tests/providers/test_internet.py
index 158a83b771..8f64139b62 100644
--- a/tests/providers/test_internet.py
+++ b/tests/providers/test_internet.py
@@ -202,3 +202,37 @@ def test_ascii_company_email(self):
email = self.factory.ascii_company_email()
validate_email(email, check_deliverability=False)
self.assertEqual(email.split('@')[0], 'asyl')
+
+
+class TestPtBR(unittest.TestCase):
+
+ def setUp(self):
+ self.factory = Faker('pt_BR')
+ self.provider = self.factory.provider('faker.providers.internet')
+
+ @mock.patch(
+ 'faker.providers.internet.Provider.user_name',
+ lambda x: 'VitóriaMagalhães'
+ )
+ def test_ascii_safe_email(self):
+ email = self.factory.ascii_safe_email()
+ validate_email(email, check_deliverability=False)
+ self.assertEqual(email.split('@')[0], 'vitoriamagalhaes')
+
+ @mock.patch(
+ 'faker.providers.internet.Provider.user_name',
+ lambda x: 'JoãoSimões'
+ )
+ def test_ascii_free_email(self):
+ email = self.factory.ascii_free_email()
+ validate_email(email, check_deliverability=False)
+ self.assertEqual(email.split('@')[0], 'joaosimoes')
+
+ @mock.patch(
+ 'faker.providers.internet.Provider.user_name',
+ lambda x: 'AndréCauã'
+ )
+ def test_ascii_company_email(self):
+ email = self.factory.ascii_company_email()
+ validate_email(email, check_deliverability=False)
+ self.assertEqual(email.split('@')[0], 'andrecaua')
|
fidals__shopelectro-693 | tests_selenium.py:976: Resurrect test `test_cart_page_open`
The puzzle `473-5159ab9c` from #473 has to be resolved:
https://github.com/fidals/shopelectro/blob/f7dc2793dc5c7eddb2e68a68368337d77ba3139e/shopelectro/tests/tests_selenium.py#L976-L976
The puzzle was created by duker33 on 08-Aug-18.
Estimate: 15 minutes,
If you have any technical questions, don't ask me, submit new tickets instead. The task will be "done" when the problem is fixed and the text of the puzzle is _removed_ from the source code. Here is more about [PDD](http://www.yegor256.com/2009/03/04/pdd.html) and [about me](http://www.yegor256.com/2017/04/05/pdd-in-action.html).
| [
{
"content": "import random\nimport string\nimport typing\nfrom uuid import uuid4\n\nfrom django.conf import settings\nfrom django.db import models\nfrom django.urls import reverse\nfrom django.utils.translation import ugettext_lazy as _\nimport mptt\n\nfrom catalog import models as catalog_models\nfrom ecommer... | [
{
"content": "import random\nimport string\nimport typing\nfrom uuid import uuid4\n\nfrom django.conf import settings\nfrom django.db import models\nfrom django.urls import reverse\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom catalog import models as catalog_models\nfrom ecommerce import mod... | diff --git a/shopelectro/models.py b/shopelectro/models.py
index 6f5320ce..ec837263 100644
--- a/shopelectro/models.py
+++ b/shopelectro/models.py
@@ -7,7 +7,6 @@
from django.db import models
from django.urls import reverse
from django.utils.translation import ugettext_lazy as _
-import mptt
from catalog import models as catalog_models
from ecommerce import models as ecommerce_models
diff --git a/shopelectro/tests/tests_selenium.py b/shopelectro/tests/tests_selenium.py
index c5df3ead..dea48555 100644
--- a/shopelectro/tests/tests_selenium.py
+++ b/shopelectro/tests/tests_selenium.py
@@ -16,7 +16,6 @@
from selenium.webdriver.support import expected_conditions as EC, ui
from pages.models import FlatPage, CustomPage
-
from shopelectro.models import Category, Product
from shopelectro.tests import helpers
@@ -57,6 +56,8 @@ def is_cart_empty(browser):
return browser.find_element_by_class_name('js-cart-is-empty').is_displayed()
+# @todo #494:15m Rm `wait_page_loading` function in favor of analogous method.
+# In favor of `helpers.SeleniumTestCase.wait_page_loaded`
def wait_page_loading(browser):
ui.WebDriverWait(browser, 60).until(
EC.visibility_of_element_located(
@@ -1026,8 +1027,6 @@ def test_full_buy_goal(self):
self.assertTrue('FULL_BUY_SEND' in self.reached_goals)
self.assertTrue('CMN_BUY_SEND' in self.reached_goals)
- # @todo #473:15m Resurrect test `test_cart_page_open`
- @unittest.skip
def test_cart_page_open(self):
self.buy_product()
self.prevent_default('click', '.js-go-to-cart')
|
hpcaitech__ColossalAI-5433 | [tensor] fix some unittests
[tensor] fix some unittests
[tensor] fix some unittests
| [
{
"content": "from ..cuda_extension import _CudaExtension\nfrom ..utils import get_cuda_cc_flag\n\n\nclass InferenceOpsCudaExtension(_CudaExtension):\n def __init__(self):\n super().__init__(name=\"inference_ops_cuda\")\n\n def sources_files(self):\n ret = [\n self.csrc_abs_path(f... | [
{
"content": "from ..cuda_extension import _CudaExtension\nfrom ..utils import get_cuda_cc_flag\n\n\nclass InferenceOpsCudaExtension(_CudaExtension):\n def __init__(self):\n super().__init__(name=\"inference_ops_cuda\")\n\n def sources_files(self):\n ret = [\n self.csrc_abs_path(f... | diff --git a/extensions/csrc/cuda/activation_kernel.cu b/extensions/csrc/cuda/activation_kernel.cu
new file mode 100644
index 000000000000..4121b67fc523
--- /dev/null
+++ b/extensions/csrc/cuda/activation_kernel.cu
@@ -0,0 +1,65 @@
+#include <ATen/cuda/CUDAContext.h>
+#include <torch/extension.h>
+#include <stdio.h>
+
+#include "type_shim.h"
+#include "include/mp_type_traits.h"
+
+template<typename T>
+__device__ __forceinline__ T silu_kernel(const T& x) {
+ // x * sigmoid(x)
+ using MT = typename infer::dtype::MPTypeTrait<T>::Type;
+ return static_cast<T>((static_cast<MT>(x)) / (static_cast<MT>(1.0f) + expf(static_cast<MT>(-x))));
+}
+
+template<typename scalar_t, scalar_t (*ACT_FN)(const scalar_t&)>
+__global__ void act_and_mul_kernel(
+ const scalar_t* __restrict__ ins_data,
+ scalar_t* __restrict__ outs_data,
+ const int64_t numel) {
+ using MT = typename infer::dtype::MPTypeTrait<scalar_t>::Type;
+
+ int64_t idx = static_cast<int64_t>(threadIdx.x) + static_cast<int64_t>(blockIdx.x) * static_cast<int64_t>(blockDim.x);
+ const int64_t grid_size = blockDim.x * gridDim.x;
+ if(idx > numel) {
+ return;
+ }
+
+ for(int64_t i = idx; i < numel; i += grid_size) {
+ scalar_t x = ins_data[i];
+ scalar_t y = ins_data[i+numel];
+ outs_data[i] = static_cast<scalar_t>(static_cast<MT>(ACT_FN(x)) * static_cast<MT>(y));
+ }
+}
+
+// Note(LiuYang):This func is designed for calculation mode like
+// silu(x[:half_1stdim]) * (x[half_1stdim:])
+torch::Tensor silu_and_mul(const torch::Tensor& ins)
+{
+ auto ins_shape = ins.sizes().vec();
+
+ ins_shape[0] = ins_shape[0]/2;
+ auto outs = torch::zeros(ins_shape,ins.options());
+ auto outs_shape = ins.sizes().vec();
+
+ const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+ // Note(Liuyang): numel of ins must be divisible by 2
+ int64_t numel = ((torch::numel(ins)) >> 1);
+
+ // TODO(LiuYang): Maybe we need to implement a function to get launch config
+ dim3 grid((numel+255)/256);
+ dim3 block(256);
+
+ DISPATCH_FLOAT_HALF_AND_BFLOAT(
+ ins.scalar_type(),
+ "silu_and_mul",
+ act_and_mul_kernel<scalar_t,silu_kernel<scalar_t>><<<grid, block, 0, stream>>>(
+ ins.data_ptr<scalar_t>(),
+ outs.data_ptr<scalar_t>(),
+ numel
+ );)
+
+ AT_CUDA_CHECK(cudaGetLastError());
+ return outs;
+}
diff --git a/extensions/csrc/cuda/colossal_inference_C_frontend.cpp b/extensions/csrc/cuda/colossal_inference_C_frontend.cpp
index ae410c14ff84..cc53d8b8800b 100644
--- a/extensions/csrc/cuda/colossal_inference_C_frontend.cpp
+++ b/extensions/csrc/cuda/colossal_inference_C_frontend.cpp
@@ -9,7 +9,10 @@ void decode_kv_cache_memcpy(
torch::Tensor& sequence_lengths, // [batch_size]
torch::Tensor& block_tables); // [batch_size, max_seq_len]
+torch::Tensor silu_and_mul(const torch::Tensor& ins);
+
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("decode_kv_cache_memcpy", &decode_kv_cache_memcpy,
"Copy the GPU memory of kvcache during the decode stage.");
+ m.def("silu_and_mul", &silu_and_mul, "Silu with a following multiply");
}
diff --git a/extensions/csrc/cuda/include/mp_type_traits.h b/extensions/csrc/cuda/include/mp_type_traits.h
new file mode 100644
index 000000000000..6b3ae9c1b218
--- /dev/null
+++ b/extensions/csrc/cuda/include/mp_type_traits.h
@@ -0,0 +1,35 @@
+#pragma once
+
+#include <ATen/ATen.h>
+
+#include "../type_shim.h"
+
+namespace infer {
+namespace dtype {
+
+template <typename T>
+class MPTypeTrait {
+ public:
+ using Type = float;
+};
+
+template <>
+class MPTypeTrait<float> {
+ public:
+ using Type = float;
+};
+
+template <>
+class MPTypeTrait<at::Half> {
+ public:
+ using Type = float;
+};
+
+template <>
+class MPTypeTrait<at::BFloat16> {
+ public:
+ using Type = float;
+};
+
+} // namespace dtype
+} // namespace infer
diff --git a/extensions/csrc/cuda/type_shim.h b/extensions/csrc/cuda/type_shim.h
index 5116319358d7..7be3fab1b574 100644
--- a/extensions/csrc/cuda/type_shim.h
+++ b/extensions/csrc/cuda/type_shim.h
@@ -4,6 +4,9 @@
This file is adapted from fused adam in NVIDIA/apex, commit a109f85
Licensed under the MIT License.
*/
+
+#pragma once
+
#include <ATen/ATen.h>
#include "compat.h"
diff --git a/extensions/inference/inference_ops_cuda.py b/extensions/inference/inference_ops_cuda.py
index 12bec6fab1a1..2858d716095b 100644
--- a/extensions/inference/inference_ops_cuda.py
+++ b/extensions/inference/inference_ops_cuda.py
@@ -12,6 +12,7 @@ def sources_files(self):
for fname in [
"cuda/colossal_inference_C_frontend.cpp",
"cuda/decode_kv_cache_memcpy_kernel.cu",
+ "cuda/activation_kernel.cu",
]
]
return ret
diff --git a/tests/test_infer/test_ops/cuda/test_silu_and_mul.py b/tests/test_infer/test_ops/cuda/test_silu_and_mul.py
new file mode 100644
index 000000000000..ced2db7ca048
--- /dev/null
+++ b/tests/test_infer/test_ops/cuda/test_silu_and_mul.py
@@ -0,0 +1,33 @@
+import pytest
+import torch
+
+from colossalai.kernel.kernel_loader import InferenceOpsLoader
+from colossalai.utils import get_current_device
+
+inference_ops = InferenceOpsLoader().load()
+
+
+@pytest.mark.parametrize("SHAPE_X", [2])
+@pytest.mark.parametrize("SHAPE_Y", [64])
+@pytest.mark.parametrize("SHAPE_Z", [11008])
+@pytest.mark.parametrize("dtype", [torch.float32, torch.float16])
+def test_silu_and_mul(SHAPE_X, SHAPE_Y, SHAPE_Z, dtype):
+ torch.manual_seed(5)
+ device = get_current_device()
+ ref_input = torch.randn(SHAPE_X, SHAPE_Y, SHAPE_Z, dtype=dtype, device=device)
+ origin_input = ref_input.clone()
+
+ act_out = torch.nn.functional.silu(ref_input[0], inplace=True)
+ ref_out = act_out * ref_input[1]
+
+ origin_out = inference_ops.silu_and_mul(origin_input)
+
+ if dtype == torch.float32:
+ assert torch.allclose(origin_out, ref_out, atol=1e-5, rtol=1e-5)
+ else:
+ assert torch.allclose(origin_out, ref_out, atol=1e-3, rtol=1e-3)
+
+
+if __name__ == "__main__":
+ test_silu_and_mul(2, 64, 11008, torch.float32)
+ test_silu_and_mul(2, 64, 11008, torch.float16)
|
saulpw__visidata-2398 | `history` parameter of input() is appears ignored
**Small description**
I think the `history` parameter of the `input` function is unused and overwritten here:
https://github.com/saulpw/visidata/blob/3ad7a3d0c1475ff53bf31481506b9a748b48ac7c/visidata/_input.py#L501
**Expected result**
That `history` would be honored. I believe it is not.
**Actual result with screenshot**
(no screenshot, this shows up when you call this function and use the arrow keys to go back into the history.)
**Steps to reproduce with sample data and a .vd**
Try this in your `.visidatarc`:
```
VIEWER_HISTORY = []
def guess_program(filename: str) -> str:
basename, ext = path.splitext(filename)
ext = ext.lower()
with open("/tmp/log", "w") as f:
print(ext, file=f)
if ext == '.pdf':
return 'evince'
elif ext == '.jpg':
return 'geeqie'
@visidata.VisiData.api
def launchViewer(vd, *args):
"Launch $EDITOR with *args* as arguments."
global VIEWER_HISTORY
filename = str(args[0])
if not path.exists(filename):
vd.fail(f"Value '{filename}' is not an existing filename.")
program = guess_program(filename)
if program and (not VIEWER_HISTORY or VIEWER_HISTORY[0] != program):
VIEWER_HISTORY.append(program)
viewer = vd.input(
"viewer: ",
history=VIEWER_HISTORY,
defaultLast=bool(VIEWER_HISTORY),
)
if not viewer.strip():
vd.fail("Viewer not provided")
elif viewer not in VIEWER_HISTORY:
VIEWER_HISTORY.insert(0, viewer)
args = viewer.split() + list(args)
with SuspendCurses():
# subprocess.call(args)
cproc = subprocess.Popen(args)
DirSheet.addCommand(
"Alt+!",
"sysopen-row-shell-command",
"launchViewer(cursorRow)",
"open current file in external program (input manually)",
)
```
**Additional context**
git reflog: 3ad7a3d0c1475ff53bf31481506b9a748b48ac7c
date: 2024-04-28
python: 3.12.1
Thank you thank you!
| [
{
"content": "from contextlib import suppress\nimport curses\n\nimport visidata\n\nfrom visidata import EscapeException, ExpectedException, clipdraw, Sheet, VisiData, BaseSheet\nfrom visidata import vd, options, colors, dispwidth, ColorAttr\nfrom visidata import AttrDict\n\n\nvd.theme_option('color_edit_unfocus... | [
{
"content": "from contextlib import suppress\nimport curses\n\nimport visidata\n\nfrom visidata import EscapeException, ExpectedException, clipdraw, Sheet, VisiData, BaseSheet\nfrom visidata import vd, options, colors, dispwidth, ColorAttr\nfrom visidata import AttrDict\n\n\nvd.theme_option('color_edit_unfocus... | diff --git a/visidata/_input.py b/visidata/_input.py
index 77f922dcb..5f998f36d 100644
--- a/visidata/_input.py
+++ b/visidata/_input.py
@@ -528,7 +528,8 @@ def input(vd, prompt, type=None, defaultLast=False, history=[], dy=0, attr=None,
import getpass
return getpass.getpass(prompt)
- history = list(vd.inputHistory.setdefault(type, {}).keys())
+ if not history:
+ history = list(vd.inputHistory.setdefault(type, {}).keys())
y = sheet.windowHeight-dy-1
promptlen = dispwidth(prompt)
|
awslabs__gluonts-1537 | Theta model does not preserve item IDs
## Description
When using the `RForecastPredictor` with `method_name = "thetaf"`, the item IDs returned by the predictor's forecasts do not align with the actual item IDs. Instead, it returns `None` for the item IDs.
## To Reproduce
```python
from gluonts.dataset.repository.datasets import get_dataset
from gluonts.model.r_forecast import RForecastPredictor
from gluonts.evaluation.backtest import make_evaluation_predictions
dataset = get_dataset("m3_yearly")
predictor = RForecastPredictor(
freq=dataset.metadata.freq,
prediction_length=dataset.metadata.prediction_length,
method_name="thetaf",
)
forecast_pred, forecast_true = make_evaluation_predictions(dataset.test, predictor)
forecast_pred, _ = make_evaluation_predictions(dataset.test, predictor)
for pred in forecast_pred:
print(pred.item_id)
```
You'll see that only `None` is printed.
## Environment
- Operating system: Debian Buster
- Python version: `3.8.9`
- GluonTS version: Master, Post 0.7.0 (commit 645d551a2190b9b749528917cdf1c5e897c861d2)
- MXNet version: `1.8.0.post0`
Full list of dependencies:
```
PyYAML = "^5.4.1"
click = "^7.1.2"
fastparquet = "^0.6.1"
fbprophet = "^0.7.1"
gluonts = {git = "https://github.com/awslabs/gluon-ts.git", rev = "f6948bacb7a038df3374e768ad4939455c74b49d"}
holidays = "^0.11.1"
mxnet = "^1.8.0"
numpy = "^1.20.3"
pandas = "^1.2.4"
pyarrow = "^4.0.0"
pydantic = "^1.8.2"
pystan = "^2.0.0"
python = ">=3.8,<3.10"
rpy2 = ">=2.9.*,<3.*"
sagemaker = "^2.40.0"
sagemaker-training = "^3.9.2"
scikit-learn = "^0.24.2"
scipy = "^1.6.3"
toolz = "^0.11.1"
tqdm = "^4.60.0"
ujson = "^4.0.2"
xgboost = "^1.4.1"
```
| [
{
"content": "# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\").\n# You may not use this file except in compliance with the License.\n# A copy of the License is located at\n#\n# http://www.apache.org/licenses/LICE... | [
{
"content": "# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\").\n# You may not use this file except in compliance with the License.\n# A copy of the License is located at\n#\n# http://www.apache.org/licenses/LICE... | diff --git a/src/gluonts/model/r_forecast/_predictor.py b/src/gluonts/model/r_forecast/_predictor.py
index 928d150173..6b3010f076 100644
--- a/src/gluonts/model/r_forecast/_predictor.py
+++ b/src/gluonts/model/r_forecast/_predictor.py
@@ -207,5 +207,9 @@ def predict(
else None
)
yield SampleForecast(
- samples, forecast_start(data), self.freq, info=info
+ samples,
+ forecast_start(data),
+ self.freq,
+ info=info,
+ item_id=entry.get("item_id", None),
)
|
fossasia__open-event-server-6254 | Verify any user automatically if clicks on a reset password link
If a user is using reset password link to reset his password, he is by default verifying his account
| [
{
"content": "import base64\nimport base64\nimport logging\nimport random\nimport string\nfrom functools import wraps\n\nimport requests\nfrom flask import request, jsonify, make_response, Blueprint, send_file\nfrom flask_jwt_extended import jwt_required, current_user, create_access_token\nfrom flask_limiter.ut... | [
{
"content": "import base64\nimport base64\nimport logging\nimport random\nimport string\nfrom functools import wraps\n\nimport requests\nfrom flask import request, jsonify, make_response, Blueprint, send_file\nfrom flask_jwt_extended import jwt_required, current_user, create_access_token\nfrom flask_limiter.ut... | diff --git a/app/api/auth.py b/app/api/auth.py
index a4cf7fa4ce..35b9eb6d2d 100644
--- a/app/api/auth.py
+++ b/app/api/auth.py
@@ -278,7 +278,7 @@ def reset_password_patch():
return NotFoundError({'source': ''}, 'User Not Found').respond()
else:
user.password = password
- if user.was_registered_with_order:
+ if not user.is_verified:
user.is_verified = True
save_to_db(user)
|
ycm-core__ycmd-542 | Why is clang_completer requesting `unloaded_buffer` key in OnBufferUnload
Would it be possible to just reuse `filepath` in `OnBufferUnload` in `clang_completer` instead of having to specify `unloaded_buffer`. The typescript completer is already using `filepath`. It would be nice to align both.
| [
{
"content": "# Copyright (C) 2011, 2012 Google Inc.\n#\n# This file is part of ycmd.\n#\n# ycmd is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option... | [
{
"content": "# Copyright (C) 2011, 2012 Google Inc.\n#\n# This file is part of ycmd.\n#\n# ycmd is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option... | diff --git a/ycmd/completers/cpp/clang_completer.py b/ycmd/completers/cpp/clang_completer.py
index 35bb660c22..37e44a4090 100755
--- a/ycmd/completers/cpp/clang_completer.py
+++ b/ycmd/completers/cpp/clang_completer.py
@@ -338,7 +338,7 @@ def OnFileReadyToParse( self, request_data ):
def OnBufferUnload( self, request_data ):
self._completer.DeleteCachesForFile(
- ToCppStringCompatible( request_data[ 'unloaded_buffer' ] ) )
+ ToCppStringCompatible( request_data[ 'filepath' ] ) )
def GetDetailedDiagnostic( self, request_data ):
diff --git a/ycmd/tests/typescript/event_notification_test.py b/ycmd/tests/typescript/event_notification_test.py
new file mode 100644
index 0000000000..a8402c6c34
--- /dev/null
+++ b/ycmd/tests/typescript/event_notification_test.py
@@ -0,0 +1,116 @@
+# Copyright (C) 2016 ycmd contributors
+#
+# This file is part of ycmd.
+#
+# ycmd is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# ycmd is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with ycmd. If not, see <http://www.gnu.org/licenses/>.
+
+from __future__ import absolute_import
+from __future__ import unicode_literals
+from __future__ import print_function
+from __future__ import division
+from future import standard_library
+standard_library.install_aliases()
+from builtins import * # noqa
+
+from hamcrest import assert_that, contains, has_entries
+
+from ycmd.tests.typescript import IsolatedYcmd, PathToTestFile
+from ycmd.tests.test_utils import ( BuildRequest, ClearCompletionsCache,
+ CompletionEntryMatcher )
+from ycmd.utils import ReadFile
+
+
+@IsolatedYcmd
+def EventNotification_OnBufferUnload_CloseFile_test( app ):
+ # Open main.ts file in a buffer.
+ main_filepath = PathToTestFile( 'buffer_unload', 'main.ts' )
+ main_contents = ReadFile( main_filepath )
+
+ event_data = BuildRequest( filepath = main_filepath,
+ filetype = 'typescript',
+ contents = main_contents,
+ event_name = 'BufferVisit' )
+ app.post_json( '/event_notification', event_data )
+
+ # Complete in main.ts buffer an object defined in imported.ts.
+ completion_data = BuildRequest( filepath = main_filepath,
+ filetype = 'typescript',
+ contents = main_contents,
+ line_num = 3,
+ column_num = 10 )
+ response = app.post_json( '/completions', completion_data )
+ assert_that( response.json, has_entries( {
+ 'completions': contains( CompletionEntryMatcher( 'method' ) ) } ) )
+ # FIXME: we should not have to clear the cache.
+ ClearCompletionsCache()
+
+ # Open imported.ts file in another buffer.
+ imported_filepath = PathToTestFile( 'buffer_unload', 'imported.ts' )
+ imported_contents = ReadFile( imported_filepath )
+
+ event_data = BuildRequest( filepath = imported_filepath,
+ filetype = 'typescript',
+ contents = imported_contents,
+ event_name = 'BufferVisit' )
+ app.post_json( '/event_notification', event_data )
+
+ # Modify imported.ts buffer without writing the changes to disk.
+ modified_imported_contents = imported_contents.replace( 'method',
+ 'modified_method' )
+
+ # FIXME: TypeScript completer should not rely on the FileReadyToParse events
+ # to synchronize the contents of dirty buffers but use instead the file_data
+ # field of the request.
+ event_data = BuildRequest( filepath = imported_filepath,
+ filetype = 'typescript',
+ contents = modified_imported_contents,
+ event_name = 'FileReadyToParse' )
+ app.post_json( '/event_notification', event_data )
+
+ # Complete at same location in main.ts buffer.
+ imported_data = {
+ imported_filepath: {
+ 'filetypes': [ 'typescript' ],
+ 'contents': modified_imported_contents
+ }
+ }
+ completion_data = BuildRequest( filepath = main_filepath,
+ filetype = 'typescript',
+ contents = main_contents,
+ line_num = 3,
+ column_num = 10,
+ file_data = imported_data )
+ response = app.post_json( '/completions', completion_data )
+ assert_that( response.json, has_entries( {
+ 'completions': contains( CompletionEntryMatcher( 'modified_method' ) ) } )
+ )
+ # FIXME: we should not have to clear the cache.
+ ClearCompletionsCache()
+
+ # Unload imported.ts buffer.
+ event_data = BuildRequest( filepath = imported_filepath,
+ filetype = 'typescript',
+ contents = imported_contents,
+ event_name = 'BufferUnload' )
+ app.post_json( '/event_notification', event_data )
+
+ # Complete at same location in main.ts buffer.
+ completion_data = BuildRequest( filepath = main_filepath,
+ filetype = 'typescript',
+ contents = main_contents,
+ line_num = 3,
+ column_num = 10 )
+ response = app.post_json( '/completions', completion_data )
+ assert_that( response.json, has_entries( {
+ 'completions': contains( CompletionEntryMatcher( 'method' ) ) } ) )
diff --git a/ycmd/tests/typescript/testdata/buffer_unload/imported.ts b/ycmd/tests/typescript/testdata/buffer_unload/imported.ts
new file mode 100644
index 0000000000..ffb7e58533
--- /dev/null
+++ b/ycmd/tests/typescript/testdata/buffer_unload/imported.ts
@@ -0,0 +1,4 @@
+export class Imported {
+ method() {
+ }
+}
diff --git a/ycmd/tests/typescript/testdata/buffer_unload/main.ts b/ycmd/tests/typescript/testdata/buffer_unload/main.ts
new file mode 100644
index 0000000000..6c26d9e899
--- /dev/null
+++ b/ycmd/tests/typescript/testdata/buffer_unload/main.ts
@@ -0,0 +1,3 @@
+import { Imported } from "./imported";
+let imported = new Imported();
+imported.
|
getmoto__moto-1671 | IoT service list_things should include thing ARNs
### Actual
`list_things` method of the AWS IoT service returns _thingName_, _attributes_, _thingTypeName_ and _version_
### Expected
The `list_things` method of the AWS IoT service should include the thing ARN (_thingArn_) as described [here](https://boto3.readthedocs.io/en/latest/reference/services/iot.html#IoT.Client.list_things)
| [
{
"content": "from __future__ import unicode_literals\nimport time\nimport boto3\nimport string\nimport random\nimport hashlib\nimport uuid\nimport re\nfrom datetime import datetime\nfrom moto.core import BaseBackend, BaseModel\nfrom collections import OrderedDict\nfrom .exceptions import (\n ResourceNotFoun... | [
{
"content": "from __future__ import unicode_literals\nimport time\nimport boto3\nimport string\nimport random\nimport hashlib\nimport uuid\nimport re\nfrom datetime import datetime\nfrom moto.core import BaseBackend, BaseModel\nfrom collections import OrderedDict\nfrom .exceptions import (\n ResourceNotFoun... | diff --git a/moto/iot/models.py b/moto/iot/models.py
index 1b10c09fc5c0..ce7a4cf57eb1 100644
--- a/moto/iot/models.py
+++ b/moto/iot/models.py
@@ -32,6 +32,7 @@ def __init__(self, thing_name, thing_type, attributes, region_name):
def to_dict(self, include_default_client_id=False):
obj = {
'thingName': self.thing_name,
+ 'thingArn': self.arn,
'attributes': self.attributes,
'version': self.version
}
diff --git a/tests/test_iot/test_iot.py b/tests/test_iot/test_iot.py
index 19e11476be77..2136157906c0 100644
--- a/tests/test_iot/test_iot.py
+++ b/tests/test_iot/test_iot.py
@@ -35,12 +35,14 @@ def test_things():
res.should.have.key('things').which.should.have.length_of(1)
for thing in res['things']:
thing.should.have.key('thingName').which.should_not.be.none
+ thing.should.have.key('thingArn').which.should_not.be.none
thing = client.update_thing(thingName=name, attributePayload={'attributes': {'k1': 'v1'}})
res = client.list_things()
res.should.have.key('things').which.should.have.length_of(1)
for thing in res['things']:
thing.should.have.key('thingName').which.should_not.be.none
+ thing.should.have.key('thingArn').which.should_not.be.none
res['things'][0]['attributes'].should.have.key('k1').which.should.equal('v1')
thing = client.describe_thing(thingName=name)
|
chainer__chainer-1850 | Build failed on Mac
I cannot build Chainer on mac after #1775 is merged.
```
% python setup.py develop
Options: {'profile': False, 'annotate': False, 'linetrace': False, 'no_cuda': False}
Traceback (most recent call last):
File "setup.py", line 17, in <module>
ext_modules = chainer_setup_build.get_ext_modules()
File "/Users/unno/git/chainer/chainer_setup_build.py", line 219, in get_ext_modules
sysconfig.customize_compiler(compiler)
File "/Users/unno/.pyenv/versions/2.7.8/lib/python2.7/distutils/sysconfig.py", line 168, in customize_compiler
if not _config_vars.get('CUSTOMIZED_OSX_COMPILER', ''):
AttributeError: 'NoneType' object has no attribute 'get'
```
I investigated distutils. It initializes `_config_var` variables as `None` and when a user calls `get_config_vars` this variable is initialized.
BTW, I think we need to use ccompiler in `build_ext` because this option has many command line options for compiler flags.
| [
{
"content": "from __future__ import print_function\nfrom distutils import ccompiler\nfrom distutils import sysconfig\nimport os\nfrom os import path\nimport sys\n\nimport pkg_resources\nimport setuptools\n\nfrom install import build\nfrom install import utils\n\n\nrequire_cython_version = pkg_resources.parse_v... | [
{
"content": "from __future__ import print_function\nfrom distutils import ccompiler\nfrom distutils import sysconfig\nimport os\nfrom os import path\nimport sys\n\nimport pkg_resources\nimport setuptools\n\nfrom install import build\nfrom install import utils\n\n\nrequire_cython_version = pkg_resources.parse_v... | diff --git a/chainer_setup_build.py b/chainer_setup_build.py
index cd763ce95d9d..82425a96e465 100644
--- a/chainer_setup_build.py
+++ b/chainer_setup_build.py
@@ -214,6 +214,9 @@ def get_ext_modules():
arg_options = parse_args()
print('Options:', arg_options)
+ # We need to call get_config_vars to initialize _config_vars in distutils
+ # see #1849
+ sysconfig.get_config_vars()
compiler = ccompiler.new_compiler()
sysconfig.customize_compiler(compiler)
|
pypa__pip-10009 | Update quickstart guide to reflect user research
Updates quickstart guide to reflect most common tasks as discovered in our "buy a feature" user research.
Preview: https://pip--9137.org.readthedocs.build/en/9137/quickstart/
| [
{
"content": "\"\"\"Sphinx configuration file for pip's documentation.\"\"\"\n\nimport glob\nimport os\nimport pathlib\nimport re\nimport sys\nfrom typing import List, Tuple\n\n# Add the docs/ directory to sys.path, because pip_sphinxext.py is there.\ndocs_dir = os.path.dirname(os.path.dirname(__file__))\nsys.p... | [
{
"content": "\"\"\"Sphinx configuration file for pip's documentation.\"\"\"\n\nimport glob\nimport os\nimport pathlib\nimport re\nimport sys\nfrom typing import List, Tuple\n\n# Add the docs/ directory to sys.path, because pip_sphinxext.py is there.\ndocs_dir = os.path.dirname(os.path.dirname(__file__))\nsys.p... | diff --git a/docs/html/conf.py b/docs/html/conf.py
index 2a4387a352a..9e210539e89 100644
--- a/docs/html/conf.py
+++ b/docs/html/conf.py
@@ -30,7 +30,7 @@
# General information about the project.
project = "pip"
-copyright = "2008-2020, PyPA"
+copyright = "The pip developers"
# Find the version and release information.
# We have a single source of truth for our version number: pip's __init__.py file.
diff --git a/docs/html/getting-started.md b/docs/html/getting-started.md
new file mode 100644
index 00000000000..42ac2c93400
--- /dev/null
+++ b/docs/html/getting-started.md
@@ -0,0 +1,104 @@
+# Getting Started
+
+To get started with using pip, you should [install Python] on your system.
+
+[install Python]: https://realpython.com/installing-python/
+
+## Ensure you have a working pip
+
+As a first step, you should check that you have a working Python with pip
+installed. This can be done by running the following commands and making
+sure that the output looks similar.
+
+```{pip-cli}
+$ python --version
+Python 3.N.N
+$ pip --version
+pip X.Y.Z from ... (python 3.N.N)
+```
+
+If that worked, congratulations! You have a working pip in your environment.
+
+If you got output that does not look like the sample above, please read
+the {doc}`installation` page. It provides guidance on how to install pip
+within a Python environment that doesn't have it.
+
+## Common tasks
+
+### Install a package
+
+```{pip-cli}
+$ pip install sampleproject
+[...]
+Successfully installed sampleproject
+```
+
+By default, pip will fetch packages from [Python Package Index][PyPI], a
+repository of software for the Python programming language where anyone can
+upload packages.
+
+[PyPI]: https://pypi.org/
+
+### Install a package from GitHub
+
+```{pip-cli}
+$ pip install git+https://github.com/pypa/sampleproject.git@main
+[...]
+Successfully installed sampleproject
+```
+
+See {ref}`VCS Support` for more information about this syntax.
+
+### Install a package from a distribution file
+
+pip can install directly from distribution files as well. They come in 2 forms:
+
+- {term}`source distribution <Source Distribution (or "sdist")>` (usually shortened to "sdist")
+- {term}`wheel distribution <Wheel>` (usually shortened to "wheel")
+
+```{pip-cli}
+$ pip install sampleproject-1.0.tar.gz
+[...]
+Successfully installed sampleproject
+$ pip install sampleproject-1.0-py3-none-any.whl
+[...]
+Successfully installed sampleproject
+```
+
+### Install multiple packages using a requirements file
+
+Many Python projects use {file}`requirements.txt` files, to specify the
+list of packages that need to be installed for the project to run. To install
+the packages listed in that file, you can run:
+
+```{pip-cli}
+$ pip install -r requirements.txt
+[...]
+Successfully installed sampleproject
+```
+
+### Upgrade a package
+
+```{pip-cli}
+$ pip install --upgrade sampleproject
+Uninstalling sampleproject:
+ [...]
+Proceed (y/n)? y
+Successfully uninstalled sampleproject
+```
+
+### Uninstall a package
+
+```{pip-cli}
+$ pip uninstall sampleproject
+Uninstalling sampleproject:
+ [...]
+Proceed (y/n)? y
+Successfully uninstalled sampleproject
+```
+
+## Next Steps
+
+It is recommended to learn about what virtual environments are and how to use
+them. This is covered in the ["Installing Packages"](pypug:tutorials/installing-packages)
+tutorial on packaging.python.org.
diff --git a/docs/html/index.md b/docs/html/index.md
index a84c2665d0e..7cf130c4bb6 100644
--- a/docs/html/index.md
+++ b/docs/html/index.md
@@ -10,8 +10,8 @@ install packages from the [Python Package Index][pypi] and other indexes.
```{toctree}
:hidden:
-quickstart
-installing
+getting-started
+installation
user_guide
cli/index
```
@@ -29,7 +29,7 @@ GitHub <https://github.com/pypa/pip>
If you want to learn about how to use pip, check out the following resources:
-- [Quickstart](quickstart)
+- [Getting Started](getting-started)
- [Python Packaging User Guide](https://packaging.python.org)
If you find bugs, need help, or want to talk to the developers, use our mailing
diff --git a/docs/html/installation.md b/docs/html/installation.md
new file mode 100644
index 00000000000..da975727185
--- /dev/null
+++ b/docs/html/installation.md
@@ -0,0 +1,78 @@
+# Installation
+
+Usually, pip is automatically installed if you are:
+
+- working in a
+ {ref}`virtual environment <pypug:Creating and using Virtual Environments>`
+- using Python downloaded from [python.org](https://www.python.org)
+- using Python that has not been modified by a redistributor to remove
+ {mod}`ensurepip`
+
+## Supported Methods
+
+If your Python environment does not have pip installed, there are 2 mechanisms
+to install pip supported directly by pip's maintainers:
+
+- [`ensurepip`](#using-ensurepip)
+- [`get-pip.py`](#using-get-pip-py)
+
+### `ensurepip`
+
+Python comes with an {mod}`ensurepip` module[^python], which can install pip in
+a Python environment.
+
+```{pip-cli}
+$ python -m ensurepip --upgrade
+```
+
+More details about how {mod}`ensurepip` works and how it can be used, is
+available in the standard library documentation.
+
+### `get-pip.py`
+
+This is a Python script that uses some bootstrapping logic to install
+pip.
+
+- Download the script, from <https://bootstrap.pypa.io/get-pip.py>.
+- Open a terminal/command prompt, `cd` to the folder containing the
+ `get-pip.py` file and run:
+
+ ```{pip-cli}
+ $ python get-pip.py
+ ```
+
+More details about this script can be found in [pypa/get-pip]'s README.
+
+[pypa/get-pip]: https://github.com/pypa/get-pip
+
+## Alternative Methods
+
+Depending on how you installed Python, there might be other mechanisms
+available to you for installing pip such as
+{ref}`using Linux package managers <pypug:installing pip/setuptools/wheel with linux package managers>`.
+
+These mechanisms are provided by redistributors of pip, who may have modified
+pip to change its behaviour. This has been a frequent source of user confusion,
+since it causes a mismatch between documented behaviour in this documentation
+and how pip works after those modifications.
+
+If you face issues when using Python and pip installed using these mechanisms,
+it is recommended to request for support from the relevant provider (eg: Linux
+distro community, cloud provider support channels, etc).
+
+## Compatibility
+
+The current version of pip works on:
+
+- Windows, Linux and MacOS.
+- CPython 3.6, 3.7, 3.8, 3.9 and latest PyPy3.
+
+pip is tested to work on the latest patch version of the Python interpreter,
+for each of the minor versions listed above. Previous patch versions are
+supported on a best effort approach.
+
+pip's maintainers do not provide support for users on older versions of Python,
+and these users should request for support from the relevant provider
+(eg: Linux distro community, cloud provider support channels, etc).
+
+[^python]: The `ensurepip` module was added to the Python standard library in Python 3.4.
diff --git a/docs/html/installing.rst b/docs/html/installing.rst
index 95b21899dc6..e8d86f3441c 100644
--- a/docs/html/installing.rst
+++ b/docs/html/installing.rst
@@ -1,230 +1,11 @@
-.. _`Installation`:
+:orphan:
-============
-Installation
-============
+.. meta::
-Do I need to install pip?
-=========================
+ :http-equiv=refresh: 3; url=../installation/
-pip is already installed if you are using Python 2 >=2.7.9 or Python 3 >=3.4
-downloaded from `python.org <https://www.python.org>`_ or if you are working
-in a :ref:`Virtual Environment <pypug:Creating and using Virtual Environments>`
-created by :ref:`pypug:virtualenv` or :ref:`venv <pypug:venv>`. Just make sure
-to :ref:`upgrade pip <Upgrading pip>`.
+This page has moved
+===================
-Use the following command to check whether pip is installed:
-
-.. tab:: Unix/macOS
-
- .. code-block:: console
-
- $ python -m pip --version
- pip X.Y.Z from .../site-packages/pip (python X.Y)
-
-.. tab:: Windows
-
- .. code-block:: console
-
- C:\> py -m pip --version
- pip X.Y.Z from ...\site-packages\pip (python X.Y)
-
-Using Linux Package Managers
-============================
-
-.. warning::
-
- If you installed Python from a package manager on Linux, you should always
- install pip for that Python installation using the same source.
-
-See `pypug:Installing pip/setuptools/wheel with Linux Package Managers <https://packaging.python.org/guides/installing-using-linux-tools/>`_
-in the Python Packaging User Guide.
-
-Here are ways to contact a few Linux package maintainers if you run into
-problems:
-
-* `Deadsnakes PPA <https://github.com/deadsnakes/issues>`_
-* `Debian Python Team <https://wiki.debian.org/Teams/PythonTeam>`_ (for general
- issues related to ``apt``)
-* `Red Hat Bugzilla <https://bugzilla.redhat.com/>`_
-
-pip developers do not have control over how Linux distributions handle pip
-installations, and are unable to provide solutions to related issues in
-general.
-
-Using ensurepip
-===============
-
-Python >=3.4 can self-bootstrap pip with the built-in
-:ref:`ensurepip <pypug:ensurepip>` module. Refer to the standard library
-documentation for more details. Make sure to :ref:`upgrade pip <Upgrading pip>`
-after ``ensurepip`` installs pip.
-
-See the `Using Linux Package Managers`_ section if your Python reports
-``No module named ensurepip`` on Debian and derived systems (e.g. Ubuntu).
-
-
-.. _`get-pip`:
-
-Installing with get-pip.py
-==========================
-
-.. warning::
-
- Be cautious if you are using a Python install that is managed by your operating
- system or another package manager. ``get-pip.py`` does not coordinate with
- those tools, and may leave your system in an inconsistent state.
-
-To manually install pip, securely [1]_ download ``get-pip.py`` by following
-this link: `get-pip.py
-<https://bootstrap.pypa.io/get-pip.py>`_. Alternatively, use ``curl``::
-
- curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
-
-Then run the following command in the folder where you
-have downloaded ``get-pip.py``:
-
-.. tab:: Unix/macOS
-
- .. code-block:: shell
-
- python get-pip.py
-
-.. tab:: Windows
-
- .. code-block:: shell
-
- py get-pip.py
-
-``get-pip.py`` also installs :ref:`pypug:setuptools` [2]_ and :ref:`pypug:wheel`
-if they are not already. :ref:`pypug:setuptools` is required to install
-:term:`source distributions <pypug:Source Distribution (or "sdist")>`. Both are
-required in order to build a :ref:`Wheel cache` (which improves installation
-speed), although neither are required to install pre-built :term:`wheels
-<pypug:Wheel>`.
-
-.. note::
-
- The get-pip.py script is supported on the same python version as pip.
- For the now unsupported Python 2.6, alternate script is available
- `here <https://bootstrap.pypa.io/2.6/get-pip.py>`__.
-
-
-get-pip.py options
-------------------
-
-.. option:: --no-setuptools
-
- If set, do not attempt to install :ref:`pypug:setuptools`
-
-.. option:: --no-wheel
-
- If set, do not attempt to install :ref:`pypug:wheel`
-
-
-``get-pip.py`` allows :ref:`pip install options <pip
-install Options>` and the :ref:`general options <General Options>`. Below are
-some examples:
-
-Install from local copies of pip and setuptools:
-
-.. tab:: Unix/macOS
-
- .. code-block:: shell
-
- python get-pip.py --no-index --find-links=/local/copies
-
-.. tab:: Windows
-
- .. code-block:: shell
-
- py get-pip.py --no-index --find-links=/local/copies
-
-Install to the user site [3]_:
-
-.. tab:: Unix/macOS
-
- .. code-block:: shell
-
- python get-pip.py --user
-
-.. tab:: Windows
-
- .. code-block:: shell
-
- py get-pip.py --user
-
-Install behind a proxy:
-
-.. tab:: Unix/macOS
-
- .. code-block:: shell
-
- python get-pip.py --proxy="http://[user:passwd@]proxy.server:port"
-
-.. tab:: Windows
-
- .. code-block:: shell
-
- py get-pip.py --proxy="http://[user:passwd@]proxy.server:port"
-
-``get-pip.py`` can also be used to install a specified combination of ``pip``,
-``setuptools``, and ``wheel`` using the same requirements syntax as pip:
-
-.. tab:: Unix/macOS
-
- .. code-block:: shell
-
- python get-pip.py pip==9.0.2 wheel==0.30.0 setuptools==28.8.0
-
-.. tab:: Windows
-
- .. code-block:: shell
-
- py get-pip.py pip==9.0.2 wheel==0.30.0 setuptools==28.8.0
-
-.. _`Upgrading pip`:
-
-Upgrading pip
-=============
-
-.. tab:: Unix/macOS
-
- .. code-block:: shell
-
- python -m pip install -U pip
-
-.. tab:: Windows
-
- .. code-block:: shell
-
- py -m pip install -U pip
-
-
-.. _compatibility-requirements:
-
-Python and OS Compatibility
-===========================
-
-pip works with CPython versions 3.6, 3.7, 3.8, 3.9 and also PyPy.
-
-This means pip works on the latest patch version of each of these minor
-versions. Previous patch versions are supported on a best effort approach.
-
-pip works on Unix/Linux, macOS, and Windows.
-
-
-----
-
-.. [1] "Secure" in this context means using a modern browser or a
- tool like ``curl`` that verifies SSL certificates when downloading from
- https URLs.
-
-.. [2] Beginning with pip v1.5.1, ``get-pip.py`` stopped requiring setuptools to
- be installed first.
-
-.. [3] The pip developers are considering making ``--user`` the default for all
- installs, including ``get-pip.py`` installs of pip, but at this time,
- ``--user`` installs for pip itself, should not be considered to be fully
- tested or endorsed. For discussion, see `Issue 1668
- <https://github.com/pypa/pip/issues/1668>`_.
+You should be redirected automatically in 3 seconds. If that didn't
+work, here's a link: :doc:`installation`
diff --git a/docs/html/quickstart.rst b/docs/html/quickstart.rst
index 96602a7b316..4385f4a7394 100644
--- a/docs/html/quickstart.rst
+++ b/docs/html/quickstart.rst
@@ -1,136 +1,11 @@
-==========
-Quickstart
-==========
+:orphan:
-First, :doc:`install pip <installing>`.
+.. meta::
-Install a package from `PyPI`_:
+ :http-equiv=refresh: 3; url=../getting-started/
-.. tab:: Unix/macOS
+This page has moved
+===================
- .. code-block:: console
-
- $ python -m pip install SomePackage
- [...]
- Successfully installed SomePackage
-
-.. tab:: Windows
-
- .. code-block:: console
-
- C:\> py -m pip install SomePackage
- [...]
- Successfully installed SomePackage
-
-
-Install a package that's already been downloaded from `PyPI`_ or
-obtained from elsewhere. This is useful if the target machine does not have a
-network connection:
-
-.. tab:: Unix/macOS
-
- .. code-block:: console
-
- $ python -m pip install SomePackage-1.0-py2.py3-none-any.whl
- [...]
- Successfully installed SomePackage
-
-.. tab:: Windows
-
- .. code-block:: console
-
- C:\> py -m pip install SomePackage-1.0-py2.py3-none-any.whl
- [...]
- Successfully installed SomePackage
-
-Show what files were installed:
-
-.. tab:: Unix/macOS
-
- .. code-block:: console
-
- $ python -m pip show --files SomePackage
- Name: SomePackage
- Version: 1.0
- Location: /my/env/lib/pythonx.x/site-packages
- Files:
- ../somepackage/__init__.py
- [...]
-
-.. tab:: Windows
-
- .. code-block:: console
-
- C:\> py -m pip show --files SomePackage
- Name: SomePackage
- Version: 1.0
- Location: /my/env/lib/pythonx.x/site-packages
- Files:
- ../somepackage/__init__.py
- [...]
-
-List what packages are outdated:
-
-.. tab:: Unix/macOS
-
- .. code-block:: console
-
- $ python -m pip list --outdated
- SomePackage (Current: 1.0 Latest: 2.0)
-
-.. tab:: Windows
-
- .. code-block:: console
-
- C:\> py -m pip list --outdated
- SomePackage (Current: 1.0 Latest: 2.0)
-
-Upgrade a package:
-
-.. tab:: Unix/macOS
-
- .. code-block:: console
-
- $ python -m pip install --upgrade SomePackage
- [...]
- Found existing installation: SomePackage 1.0
- Uninstalling SomePackage:
- Successfully uninstalled SomePackage
- Running setup.py install for SomePackage
- Successfully installed SomePackage
-
-.. tab:: Windows
-
- .. code-block:: console
-
- C:\> py -m pip install --upgrade SomePackage
- [...]
- Found existing installation: SomePackage 1.0
- Uninstalling SomePackage:
- Successfully uninstalled SomePackage
- Running setup.py install for SomePackage
- Successfully installed SomePackage
-
-Uninstall a package:
-
-.. tab:: Unix/macOS
-
- .. code-block:: console
-
- $ python -m pip uninstall SomePackage
- Uninstalling SomePackage:
- /my/env/lib/pythonx.x/site-packages/somepackage
- Proceed (y/n)? y
- Successfully uninstalled SomePackage
-
-.. tab:: Windows
-
- .. code-block:: console
-
- C:\> py -m pip uninstall SomePackage
- Uninstalling SomePackage:
- /my/env/lib/pythonx.x/site-packages/somepackage
- Proceed (y/n)? y
- Successfully uninstalled SomePackage
-
-.. _PyPI: https://pypi.org/
+You should be redirected automatically in 3 seconds. If that didn't
+work, here's a link: :doc:`getting-started`
|
aws__aws-cli-579 | Docs: s3api restore-object
Regarding the documentation here: http://docs.aws.amazon.com/cli/latest/reference/s3api/restore-object.html
I am having a hard time figuring out what the different arguments are for and what their values should be. These docs just look a little sparse. I'll see if I can find some time to help add to them where I can.
Thanks,
-Seth
| [
{
"content": "# Copyright 2013 Amazon.com, Inc. or its affiliates. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"). You\n# may not use this file except in compliance with the License. A copy of\n# the License is located at\n#\n# http://aws.amazon.com/apache2.0/\n#... | [
{
"content": "# Copyright 2013 Amazon.com, Inc. or its affiliates. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"). You\n# may not use this file except in compliance with the License. A copy of\n# the License is located at\n#\n# http://aws.amazon.com/apache2.0/\n#... | diff --git a/awscli/argprocess.py b/awscli/argprocess.py
index 26bef03c1509..da3b623fb6a1 100644
--- a/awscli/argprocess.py
+++ b/awscli/argprocess.py
@@ -303,7 +303,7 @@ def _docs_special_key_value_parse(self, param):
# should be skipped for this arg.
return None
else:
- self._docs_key_value_parse(param)
+ return self._docs_key_value_parse(param)
def _docs_key_value_parse(self, param):
s = '%s ' % param.cli_name
diff --git a/tests/unit/docs/test_help_output.py b/tests/unit/docs/test_help_output.py
index b78eed8b2967..83c3768d32d2 100644
--- a/tests/unit/docs/test_help_output.py
+++ b/tests/unit/docs/test_help_output.py
@@ -230,6 +230,15 @@ def test_no_examples_for_structure_single_scalar(self):
self.assert_not_contains('"Value": "string"')
self.assert_not_contains('Value=string')
+ def test_example_for_single_structure_not_named_value(self):
+ # Verify that if a structure does match our special case
+ # (single element named "Value"), then we still document
+ # the example syntax.
+ self.driver.main(['s3api', 'restore-object', 'help'])
+ self.assert_contains('Days=value')
+ # Also should see the JSON syntax in the help output.
+ self.assert_contains('"Days": integer')
+
class TestJSONListScalarDocs(BaseAWSHelpOutputTest):
def test_space_separated_list_docs(self):
|
buildbot__buildbot-3531 | REST API: way to query lists for "tag1" or "tag2" (as opposed to and)
The query `/api/v2/builders?tags__contains=tag1&tags__contains=tag2` returns only builders that have *both* tags `tag1` and `tag2`. I don't see a way to query for builders that have either `tag1` or `tag2`.
| [
{
"content": "# This file is part of Buildbot. Buildbot is free software: you can\n# redistribute it and/or modify it under the terms of the GNU General Public\n# License as published by the Free Software Foundation, version 2.\n#\n# This program is distributed in the hope that it will be useful, but WITHOUT\n... | [
{
"content": "# This file is part of Buildbot. Buildbot is free software: you can\n# redistribute it and/or modify it under the terms of the GNU General Public\n# License as published by the Free Software Foundation, version 2.\n#\n# This program is distributed in the hope that it will be useful, but WITHOUT\n... | diff --git a/master/buildbot/data/resultspec.py b/master/buildbot/data/resultspec.py
index 37b0192c8fb6..b275199a966f 100644
--- a/master/buildbot/data/resultspec.py
+++ b/master/buildbot/data/resultspec.py
@@ -46,7 +46,7 @@ class FieldBase(object):
plural_operators = {
'eq': lambda d, v: d in v,
'ne': lambda d, v: d not in v,
- 'contains': lambda d, v: set(v) <= set(d),
+ 'contains': lambda d, v: len(set(v).intersection(set(d))) > 0,
}
def __init__(self, field, op, values):
diff --git a/master/buildbot/newsfragments/rest_contains.bugfix b/master/buildbot/newsfragments/rest_contains.bugfix
new file mode 100644
index 000000000000..2bf033da6eb1
--- /dev/null
+++ b/master/buildbot/newsfragments/rest_contains.bugfix
@@ -0,0 +1 @@
+Make REST API's filter __contains use OR connector rather than AND according to what the documentation suggests.
\ No newline at end of file
diff --git a/master/buildbot/newsfragments/rest_contains.doc b/master/buildbot/newsfragments/rest_contains.doc
new file mode 100644
index 000000000000..7061a8a79502
--- /dev/null
+++ b/master/buildbot/newsfragments/rest_contains.doc
@@ -0,0 +1 @@
+Improve documentation of REST API's __contains filter.
diff --git a/master/buildbot/test/unit/test_data_builders.py b/master/buildbot/test/unit/test_data_builders.py
index 6af925160204..8ac49a331521 100644
--- a/master/buildbot/test/unit/test_data_builders.py
+++ b/master/buildbot/test/unit/test_data_builders.py
@@ -22,6 +22,7 @@
from twisted.trial import unittest
from buildbot.data import builders
+from buildbot.data import resultspec
from buildbot.test.fake import fakedb
from buildbot.test.fake import fakemaster
from buildbot.test.util import endpoint
@@ -106,6 +107,15 @@ def setUp(self):
return self.db.insertTestData([
fakedb.Builder(id=1, name=u'buildera'),
fakedb.Builder(id=2, name=u'builderb'),
+ fakedb.Builder(id=3, name=u'builderTagA'),
+ fakedb.Builder(id=4, name=u'builderTagB'),
+ fakedb.Builder(id=5, name=u'builderTagAB'),
+ fakedb.Tag(id=3, name=u"tagA"),
+ fakedb.Tag(id=4, name=u"tagB"),
+ fakedb.BuildersTags(builderid=3, tagid=3),
+ fakedb.BuildersTags(builderid=4, tagid=4),
+ fakedb.BuildersTags(builderid=5, tagid=3),
+ fakedb.BuildersTags(builderid=5, tagid=4),
fakedb.Master(id=13),
fakedb.BuilderMaster(id=1, builderid=2, masterid=13),
])
@@ -120,7 +130,7 @@ def test_get(self):
def check(builders):
[self.validateData(b) for b in builders]
self.assertEqual(sorted([b['builderid'] for b in builders]),
- [1, 2])
+ [1, 2, 3, 4, 5])
return d
def test_get_masterid(self):
@@ -142,6 +152,45 @@ def check(builders):
[])
return d
+ def test_get_contains_one_tag(self):
+ resultSpec = resultspec.ResultSpec(
+ filters=[resultspec.Filter('tags', 'contains', ["tagA"])])
+ d = self.callGet(('builders',))
+
+ @d.addCallback
+ def check(builders):
+ builders = resultSpec.apply(builders)
+ [self.validateData(b) for b in builders]
+ self.assertEqual(sorted([b['builderid'] for b in builders]),
+ [3, 5])
+ return d
+
+ def test_get_contains_two_tags(self):
+ resultSpec = resultspec.ResultSpec(
+ filters=[resultspec.Filter('tags', 'contains', ["tagA", "tagB"])])
+ d = self.callGet(('builders',))
+
+ @d.addCallback
+ def check(builders):
+ builders = resultSpec.apply(builders)
+ [self.validateData(b) for b in builders]
+ self.assertEqual(sorted([b['builderid'] for b in builders]),
+ [3, 4, 5])
+ return d
+
+ def test_get_contains_two_tags_one_unknown(self):
+ resultSpec = resultspec.ResultSpec(
+ filters=[resultspec.Filter('tags', 'contains', ["tagA", "tagC"])])
+ d = self.callGet(('builders',))
+
+ @d.addCallback
+ def check(builders):
+ builders = resultSpec.apply(builders)
+ [self.validateData(b) for b in builders]
+ self.assertEqual(sorted([b['builderid'] for b in builders]),
+ [3, 5])
+ return d
+
class Builder(interfaces.InterfaceTests, unittest.TestCase):
diff --git a/master/buildbot/test/unit/test_data_resultspec.py b/master/buildbot/test/unit/test_data_resultspec.py
index c87160b9c655..e43c48a77487 100644
--- a/master/buildbot/test/unit/test_data_resultspec.py
+++ b/master/buildbot/test/unit/test_data_resultspec.py
@@ -76,6 +76,16 @@ def test_ge(self):
self.assertEqual(list(f.apply(mklist('num', 5, 10, 15))),
mklist('num', 10, 15))
+ def test_contains(self):
+ f = resultspec.Filter('num', 'contains', [10])
+ self.assertEqual(list(f.apply(mklist('num', [5, 1], [10, 1], [15, 1]))),
+ mklist('num', [10, 1]))
+
+ def test_contains_plural(self):
+ f = resultspec.Filter('num', 'contains', [10, 5])
+ self.assertEqual(list(f.apply(mklist('num', [5, 1], [10, 1], [15, 1]))),
+ mklist('num', [5, 1], [10, 1]))
+
class ResultSpec(unittest.TestCase):
diff --git a/master/docs/developer/rest.rst b/master/docs/developer/rest.rst
index 5e5c4d0ce88f..664ce78d56b7 100644
--- a/master/docs/developer/rest.rst
+++ b/master/docs/developer/rest.rst
@@ -112,7 +112,8 @@ Filters can use any of the operators listed below, with query parameters of the
``ge``
select resources where the field's value is greater than or equal to ``{value}``
``contains``
- select resources where the field's value contains ``{value}``
+ Select resources where the field's value contains ``{value}``.
+ If the parameter is provided multiple times, results containing at least one of the values are returned (so `foo__contains=x&foo__contains=y` would match resources where foo contains `x`, `y` or both).
For example:
|
sunpy__sunpy-3676 | Removing astropy_helpers section in CONTRIBUTING.rst
<!-- This comments are hidden when you submit the issue so you do not need to remove them!
Please be sure to check out our contributing guidelines: https://github.com/sunpy/sunpy/blob/master/CONTRIBUTING.rst
Please be sure to check out our code of conduct:
https://github.com/sunpy/sunpy/blob/master/CODE_OF_CONDUCT.rst -->
<!-- Please have a search on our GitHub repository to see if a similar issue has already been posted.
If a similar issue is closed, have a quick look to see if you are satisfied by the resolution.
If not please go ahead and open an issue! -->
### Description
<!-- Provide a general description of the bug. -->
As of PR https://github.com/sunpy/sunpy/pull/3598, sunpy no longer needs `astropy_helpers`, and even it is removed from the package.
I think there should not be a section of Astropy Helpers in contribution guidelines as well.
| [
{
"content": "# This file is for compatibility with astropy_helpers\nversion = 'unknown.dev'\ntry:\n from importlib_metadata import version as _version, PackageNotFoundError\n version = _version('sunpy')\nexcept ImportError:\n from pkg_resources import get_distribution, DistributionNotFound\n try:\n... | [
{
"content": null,
"path": "sunpy/version.py"
}
] | diff --git a/.pep8speaks.yml b/.pep8speaks.yml
index bd956a34c15..df9f5b68358 100644
--- a/.pep8speaks.yml
+++ b/.pep8speaks.yml
@@ -2,9 +2,6 @@ pycodestyle:
max-line-length: 100
exclude:
- setup.py
- - ez_setup.py
- - ah_bootstrap.py
- - astropy_helpers/
- docs/conf.py
- sunpy/cm/color_tables.py
descending_issues_order: True
diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst
index 1ef09a3f859..fe7ae61a6b2 100644
--- a/CONTRIBUTING.rst
+++ b/CONTRIBUTING.rst
@@ -183,31 +183,6 @@ If you get stuck or want help, just `ask here`_!
.. _SunPy repository: https://github.com/sunpy/sunpy
.. _ask here: https://riot.im/app/#/room/#sunpy-general:matrix.org
-Astropy helpers
-^^^^^^^^^^^^^^^
-
-.. warning::
-
- This is a common issue, so please be aware of this.
-
-Within SunPy is a folder called `astropy_helpers`_ and this is a git submodule.
-It is very common issue that this not setup correctly and gets added to your commits.
-
-So we recommend that you always run this at the start:
-
-.. code:: bash
-
- $ git submodule update --init
-
-This should resolve any differences in the `astropy_helpers`_ folder on your machine.
-If you use::
-
- $ git status
-
-you should hopefully see no changes for the ``astropy_helpers`` folder.
-
-.. _astropy_helpers: https://github.com/astropy/astropy-helpers
-
Checking the code you have written
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
diff --git a/MANIFEST.in b/MANIFEST.in
index 8525d843246..92c119fa4b7 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,7 +1,6 @@
include README.rst
include CHANGELOG.rst
-include ah_bootstrap.py
include setup.py
include setup.cfg
include pyproject.toml
@@ -24,23 +23,5 @@ prune build
prune docs/_build
prune docs/api
-
-# the next few stanzas are for astropy_helpers. It's derived from the
-# astropy_helpers/MANIFEST.in, but requires additional includes for the actual
-# package directory and egg-info.
-
-include astropy_helpers/README.rst
-include astropy_helpers/CHANGES.rst
-include astropy_helpers/LICENSE.rst
-recursive-include astropy_helpers/licenses *
-
-include astropy_helpers/ah_bootstrap.py
-
-recursive-include astropy_helpers/astropy_helpers *.py *.pyx *.c *.h *.rst
-recursive-include astropy_helpers/astropy_helpers.egg-info *
-
-prune astropy_helpers/build
-prune astropy_helpers/astropy_helpers/tests
-
# Globally exclude compiled files
global-exclude *.pyc *.o
diff --git a/changelog/3676.doc.rst b/changelog/3676.doc.rst
new file mode 100644
index 00000000000..ee5d957cf16
--- /dev/null
+++ b/changelog/3676.doc.rst
@@ -0,0 +1,2 @@
+Removed obselete `Astropy Helpers` submodule section in `CONTRIBUTING.rst`;
+Also removed mentions of `astropy_helpers` in all files of the project.
diff --git a/setup.cfg b/setup.cfg
index 2cb60057bf4..6a7bf1381f4 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -81,9 +81,6 @@ asdf_extensions =
#pytest11 =
# asdf = asdf.tests.schema_tester
-[ah_bootstrap]
-auto_use = True
-
[build_docs]
source-dir = docs
build-dir = docs/_build
@@ -92,7 +89,7 @@ all_files = 1
[tool:pytest]
minversion = 3.0
testpaths = "sunpy" "docs"
-norecursedirs = ".tox" "build" "docs[\/]_build" "docs[\/]generated" "*.egg-info" "astropy_helpers" "examples" "sunpy[/\]cm" ".jupyter"
+norecursedirs = ".tox" "build" "docs[\/]_build" "docs[\/]generated" "*.egg-info" "examples" "sunpy[/\]cm" ".jupyter"
doctest_plus = enabled
doctest_optionflags = NORMALIZE_WHITESPACE FLOAT_CMP ELLIPSIS
addopts = -p no:warnings --doctest-rst -m "not figure"
diff --git a/sunpy/version.py b/sunpy/version.py
deleted file mode 100644
index 74af3b873aa..00000000000
--- a/sunpy/version.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# This file is for compatibility with astropy_helpers
-version = 'unknown.dev'
-try:
- from importlib_metadata import version as _version, PackageNotFoundError
- version = _version('sunpy')
-except ImportError:
- from pkg_resources import get_distribution, DistributionNotFound
- try:
- version = get_distribution("sunpy").version
- except DistributionNotFound:
- pass
-except PackageNotFoundError:
- pass
|
Project-MONAI__MONAI-5709 | tests.test_warp HTTP Error 503
```
======================================================================
ERROR: test_grad (tests.test_warp.TestWarp)
----------------------------------------------------------------------
Traceback (most recent call last):
File "D:\a\MONAI\MONAI\tests\test_warp.py", line 102, in setUp
download_url_or_skip_test(
File "D:\a\MONAI\MONAI\tests\utils.py", line 731, in download_url_or_skip_test
download_url(*args, **kwargs)
File "D:\a\MONAI\MONAI\monai\apps\utils.py", line 203, in download_url
_download_with_progress(url, tmp_name, progress=progress)
File "D:\a\MONAI\MONAI\monai\apps\utils.py", line 114, in _download_with_progress
raise e
File "D:\a\MONAI\MONAI\monai\apps\utils.py", line 111, in _download_with_progress
urlretrieve(url, filepath)
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 247, in urlretrieve
with contextlib.closing(urlopen(url, data)) as fp:
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 222, in urlopen
return opener.open(url, data, timeout)
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 531, in open
response = meth(req, response)
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 640, in http_response
response = self.parent.error(
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 563, in error
result = self._call_chain(*args)
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 502, in _call_chain
result = func(*args)
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 755, in http_error_302
return self.parent.open(new, timeout=req.timeout)
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 531, in open
response = meth(req, response)
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 640, in http_response
response = self.parent.error(
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 569, in error
return self._call_chain(*args)
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 502, in _call_chain
result = func(*args)
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 649, in http_error_default
raise HTTPError(req.full_url, code, msg, hdrs, fp)
urllib.error.HTTPError: HTTP Error 503: Egress is over the account limit.
```
test_smartcachedataset thread hangs occasionally
**Describe the bug**
```
[2022-12-05T19:30:09.040Z]
Loading dataset: 0%| | 0/2 [00:00<?, ?it/s]
Loading dataset: 100%|██████████| 2/2 [00:00<00:00, 7577.79it/s]
[2022-12-05T19:30:09.040Z] .Finished test: test_datalist (tests.test_smartcachedataset.TestSmartCacheDataset) (0.00285s)
[2022-12-05T19:30:09.040Z] Starting test: test_set_data (tests.test_smartcachedataset.TestSmartCacheDataset)...
[2022-12-05T19:30:09.040Z]
Loading dataset: 0%| | 0/5 [00:00<?, ?it/s]
Loading dataset: 100%|██████████| 5/5 [00:00<00:00, 4430.91it/s]
[2022-12-05T21:52:01.667Z] Sending interrupt signal to process
[2022-12-05T21:52:01.669Z] Killing processes
[2022-12-05T21:52:02.262Z] kill finished with exit code 0
[2022-12-05T21:52:02.264Z] Sending interrupt signal to process
[2022-12-05T21:52:02.264Z] Killing processes
[2022-12-05T21:52:02.596Z] kill finished with exit code 2
[2022-12-05T21:52:02.824Z] Terminated
[2022-12-05T21:52:02.827Z] script returned exit code 143
```
| [
{
"content": "# Copyright (c) MONAI Consortium\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable la... | [
{
"content": "# Copyright (c) MONAI Consortium\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable la... | diff --git a/monai/handlers/checkpoint_saver.py b/monai/handlers/checkpoint_saver.py
index 014f418e2b..76f6458f3d 100644
--- a/monai/handlers/checkpoint_saver.py
+++ b/monai/handlers/checkpoint_saver.py
@@ -18,7 +18,6 @@
Events, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Events")
-
if TYPE_CHECKING:
from ignite.engine import Engine
from ignite.handlers import Checkpoint, DiskSaver
diff --git a/tests/test_convert_data_type.py b/tests/test_convert_data_type.py
index 41abd1fd8d..aff4b7d995 100644
--- a/tests/test_convert_data_type.py
+++ b/tests/test_convert_data_type.py
@@ -56,7 +56,6 @@
)
)
-
UNSUPPORTED_TYPES = {np.dtype("uint16"): torch.int32, np.dtype("uint32"): torch.int64, np.dtype("uint64"): torch.int64}
diff --git a/tests/test_hovernet_nuclear_type_post_processingd.py b/tests/test_hovernet_nuclear_type_post_processingd.py
index e6e675bc76..7f3462dddb 100644
--- a/tests/test_hovernet_nuclear_type_post_processingd.py
+++ b/tests/test_hovernet_nuclear_type_post_processingd.py
@@ -30,10 +30,8 @@
image = (x - 10) ** 2 + (y - 10) ** 2 <= 5**2
image = image[None, ...].astype("uint8")
-
TEST_CASE_1 = [{}, [{"1": [10, 10]}, np.zeros_like(image), np.zeros_like(image)]]
-
TEST_CASE = []
for p in TEST_NDARRAYS:
TEST_CASE.append([p, image] + TEST_CASE_1)
diff --git a/tests/test_safe_dtype_range.py b/tests/test_safe_dtype_range.py
index df7ca439f4..2f0b1bcefc 100644
--- a/tests/test_safe_dtype_range.py
+++ b/tests/test_safe_dtype_range.py
@@ -29,7 +29,7 @@
TESTS.append((in_type(np.array(256)), in_type(np.array(255)), np.uint8)) # type: ignore
TESTS.append((in_type(np.array(-12)), in_type(np.array(0)), np.uint8)) # type: ignore
for in_type in TEST_NDARRAYS_ALL:
- TESTS.append((in_type(np.array([[256, 255], [-12, 0]])), in_type(np.array([[255, 255], [0, 0]])), np.uint8)) # type: ignore
+ TESTS.append((in_type(np.array([[256, 255], [-12, 0]])), in_type(np.array([[255, 255], [0, 0]])), np.uint8))
TESTS_LIST: List[Tuple] = []
for in_type in TEST_NDARRAYS_ALL + (int, float):
diff --git a/tests/test_smartcachedataset.py b/tests/test_smartcachedataset.py
index 9f9043d19e..63dc1534bc 100644
--- a/tests/test_smartcachedataset.py
+++ b/tests/test_smartcachedataset.py
@@ -140,6 +140,7 @@ def test_shuffle(self):
dataset.shutdown()
+ @unittest.skip("https://github.com/Project-MONAI/MONAI/issues/5660 blocks the ci")
def test_set_data(self):
data_list1 = list(range(10))
diff --git a/tests/utils.py b/tests/utils.py
index 7b303071e8..571876a681 100644
--- a/tests/utils.py
+++ b/tests/utils.py
@@ -129,7 +129,7 @@ def skip_if_downloading_fails():
except ssl.SSLError as ssl_e:
if "decryption failed" in str(ssl_e):
raise unittest.SkipTest(f"SSL error while downloading: {ssl_e}") from ssl_e
- except RuntimeError as rt_e:
+ except (RuntimeError, OSError) as rt_e:
if "unexpected EOF" in str(rt_e):
raise unittest.SkipTest(f"error while downloading: {rt_e}") from rt_e # incomplete download
if "network issue" in str(rt_e):
|
Lightning-Universe__lightning-flash-720 | NameError: name 'K' is not defined
## 🐛 Bug
<!-- A clear and concise description of what the bug is. -->
````sh
Uncaught exception
Traceback (most recent call last):
File "main.py", line 11, in <module>
datamodule = VideoClassificationData.from_folders(
File "/home/nitin/github/video_classification/venv/lib/python3.8/site-packages/flash/core/data/data_module.py", line 540, in from_folders
return cls.from_data_source(
File "/home/nitin/github/video_classification/venv/lib/python3.8/site-packages/flash/core/data/data_module.py", line 448, in from_data_source
preprocess = preprocess or cls.preprocess_cls(
File "/home/nitin/github/video_classification/venv/lib/python3.8/site-packages/flash/video/classification/data.py", line 241, in __init__
super().__init__(
File "/home/nitin/github/video_classification/venv/lib/python3.8/site-packages/flash/core/data/process.py", line 196, in __init__
train_transform = train_transform or self._resolve_transforms(RunningStage.TRAINING)
File "/home/nitin/github/video_classification/venv/lib/python3.8/site-packages/flash/core/data/process.py", line 247, in _resolve_transforms
transforms: Optional[Dict[str, Callable]] = resolved_function()
File "/home/nitin/github/video_classification/venv/lib/python3.8/site-packages/flash/video/classification/data.py", line 307, in default_transforms
transform=K.VideoSequential(
NameError: name 'K' is not defined
````
### To Reproduce
Steps to reproduce the behavior:
````python
import os
from torch.utils.data.sampler import RandomSampler
import flash
from flash.core.data.utils import download_data
from flash.video import VideoClassificationData, VideoClassifier
# 1. Download a video clip dataset. Find more datasets at https://pytorchvideo.readthedocs.io/en/latest/data.html
download_data("https://pl-flash-data.s3.amazonaws.com/kinetics.zip")
# 2. Load the Data
datamodule = VideoClassificationData.from_folders(
train_folder=os.path.join(flash.PROJECT_ROOT, "data/kinetics/train"),
val_folder=os.path.join(flash.PROJECT_ROOT, "data/kinetics/val"),
predict_folder=os.path.join(flash.PROJECT_ROOT, "data/kinetics/predict"),
batch_size=8,
clip_sampler="uniform",
clip_duration=1,
video_sampler=RandomSampler,
decode_audio=False,
num_workers=8,
)
# 3. Build the model
model = VideoClassifier(backbone="x3d_xs", num_classes=datamodule.num_classes, pretrained=False)
# 4. Create the trainer
trainer = flash.Trainer(max_epochs=3)
# 5. Finetune the model
trainer.finetune(model, datamodule=datamodule)
# 6. Save it!
trainer.save_checkpoint("video_classification.pt")
````
<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->
### Environment
- PyTorch Version (e.g., 1.0): 1.9.0
- OS (e.g., Linux): Linux (Ubuntu 18.04)
- How you installed PyTorch: pip
- Python version: 3.8
- CUDA/cuDNN version: 11.3
- GPU models and configuration: GTX 1650
### Additional Note
<!-- Add any other context about the problem here. -->
Please update the package, `lightning-flash==0.4.0` is not compatible with `pytorchvideo==0.1.2`
| [
{
"content": "# Copyright The PyTorch Lightning team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required ... | [
{
"content": "# Copyright The PyTorch Lightning team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required ... | diff --git a/flash/core/utilities/imports.py b/flash/core/utilities/imports.py
index 621ea5bb2b..fb866a5f84 100644
--- a/flash/core/utilities/imports.py
+++ b/flash/core/utilities/imports.py
@@ -133,7 +133,7 @@ class Image(metaclass=MetaImage):
]
)
_TABULAR_AVAILABLE = _TABNET_AVAILABLE and _PANDAS_AVAILABLE
-_VIDEO_AVAILABLE = _PYTORCHVIDEO_AVAILABLE
+_VIDEO_AVAILABLE = _TORCHVISION_AVAILABLE and _PIL_AVAILABLE and _PYTORCHVIDEO_AVAILABLE and _KORNIA_AVAILABLE
_IMAGE_AVAILABLE = all(
[
_TORCHVISION_AVAILABLE,
|
huggingface__transformers-11945 | wandb integration gags during hyperparameter search
## Environment info
- transformers version: 4.6.1
- Platform: Linux-4.19.0-16-cloud-amd64-x86_64-with-glibc2.10
- Python version: 3.8.10
- PyTorch version (GPU?): 1.8.1+cu111 (True)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: yes
- Using distributed or parallel set-up in script?: no
wandb version is 0.10.26, but I don't think it matters.
### Who can help
Maybe @sgugger since this is Trainer-related; I don't know who did the wandb integration specifically.
## Information
Model I am using: custom Pytorch model.
The problem arises when using:
* [ ] the official example scripts: (probably, haven't tried)
* [x] my own modified scripts: custom training script using the Trainer
The tasks I am working on is:
* [ ] an official GLUE/SQUaD task:
* [x] my own task or dataset: custom MLM training
## To reproduce
Steps to reproduce the behavior:
1. Train a model using the Trainer with the wandb logging integration and run a hyperparameter search using Optuna (also maybe Ray, but I haven't tried with Ray)
2. After the first run, you'll get an exception like below when wandb tries to log. The issue is that the previous run has finished but a new one hasn't been started.
```
..... (first trial runs fine; logs to wandb and finishes)
wandb: Synced /home/josh/runs/hps_test: https://wandb.ai/mindful/projectname/runs/2vojg06h
5%|▌ | 1/19 [00:03<01:02, 3.47s/it][W 2021-05-30 07:41:43,979] Trial 1 failed because of the following error: Error('You must call wandb.init() before wandb.log()')
Traceback (most recent call last):
File "/home/josh/anaconda3/envs/project/lib/python3.8/site-packages/optuna/_optimize.py", line 217, in _run_trial
value_or_values = func(trial)
File "/home/josh/anaconda3/envs/project/lib/python3.8/site-packages/transformers/integrations.py", line 138, in _objective
trainer.train(resume_from_checkpoint=checkpoint, trial=trial)
File "/home/josh/anaconda3/envs/project/lib/python3.8/site-packages/transformers/trainer.py", line 1332, in train
self._maybe_log_save_evaluate(tr_loss, model, trial, epoch)
File "/home/josh/anaconda3/envs/project/lib/python3.8/site-packages/transformers/trainer.py", line 1405, in _maybe_log_save_evaluate
self.log(logs)
File "/home/josh/anaconda3/envs/project/lib/python3.8/site-packages/transformers/trainer.py", line 1692, in log
self.control = self.callback_handler.on_log(self.args, self.state, self.control, logs)
File "/home/josh/anaconda3/envs/project/lib/python3.8/site-packages/transformers/trainer_callback.py", line 371, in on_log
return self.call_event("on_log", args, state, control, logs=logs)
File "/home/josh/anaconda3/envs/project/lib/python3.8/site-packages/transformers/trainer_callback.py", line 378, in call_event
result = getattr(callback, event)(
File "/home/josh/anaconda3/envs/project/lib/python3.8/site-packages/transformers/integrations.py", line 754, in on_log
self._wandb.log({**logs, "train/global_step": state.global_step})
File "/home/josh/anaconda3/envs/project/lib/python3.8/site-packages/wandb/sdk/lib/preinit.py", line 38, in preinit_wrapper
raise wandb.Error("You must call wandb.init() before {}()".format(name))
wandb.errors.Error: You must call wandb.init() before wandb.log()
wandb: ERROR You must call wandb.init() before wandb.log()
```
## Expected behavior
wandb should just reinitialize per training run so that each run is logged separately.
Note that as far as I can tell this is a one-line fix (set `_initialized` to `False` in `WandbCallback.on_train_begin` when running an hyperparameter search) so I'll open a PR with that. I just figured there should be an issue as well for clarity.
| [
{
"content": "# Copyright 2020 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#... | [
{
"content": "# Copyright 2020 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#... | diff --git a/src/transformers/integrations.py b/src/transformers/integrations.py
index 4ab15b9d50f7..19bffe1f7a6e 100644
--- a/src/transformers/integrations.py
+++ b/src/transformers/integrations.py
@@ -713,6 +713,7 @@ def on_train_begin(self, args, state, control, model=None, **kwargs):
hp_search = state.is_hyper_param_search
if hp_search:
self._wandb.finish()
+ self._initialized = False
if not self._initialized:
self.setup(args, state, model, **kwargs)
|
jupyter__docker-stacks-1964 | [BUG] - Healthcheck fails when using proxy
### What docker image(s) are you using?
base-notebook
### Host OS system and architecture running docker image
Windows 11 as host and linux/amd64 for docker
### What Docker command are you running?
docker compose up with the following dockerfile:
```Dockerfile
version: '3.4'
services:
datamining:
container_name: xxxx
image: xxxx
build:
context: .
dockerfile: ./Dockerfile
ports:
- "8888:8888"
volumes:
- xxxx:/home/jovyan/work
environment:
- DOCKER_STACKS_JUPYTER_CMD=lab
restart: on-failure
```
### How to Reproduce the problem?
Precondition is that the machine has to operate in a corporate environment using the companies proxy.
Start the container as above.
Check the state of the container with ```docker container ls```
The container is marked as unhealthy.
### Command output
```bash session
abcdefghijk "tini -g -- start-no…" x hours ago Up x hours (unhealthy) 0.0.0.0:8888->8888/tcp xxxx
```
### Expected behavior
```abcdedfghi abcdefghijk "tini -g -- start-no…" x hours ago Up x hours (healthy) 0.0.0.0:8888->8888/tcp xxxx```
### Actual behavior
After investigating the issue the problem is that docker_healthcheck.py does not run successfully giving the following error message:
```
Traceback (most recent call last):
File "/opt/conda/lib/python3.11/site-packages/urllib3/connectionpool.py", line 790, in urlopen
response = self._make_request(
^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/urllib3/connectionpool.py", line 536, in _make_request
response = conn.getresponse()
^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/urllib3/connection.py", line 461, in getresponse
httplib_response = super().getresponse()
^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/http/client.py", line 1378, in getresponse
response.begin()
File "/opt/conda/lib/python3.11/http/client.py", line 318, in begin
version, status, reason = self._read_status()
^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/http/client.py", line 287, in _read_status
raise RemoteDisconnected("Remote end closed connection without"
http.client.RemoteDisconnected: Remote end closed connection without response
The above exception was the direct cause of the following exception:
urllib3.exceptions.ProxyError: ('Unable to connect to proxy', RemoteDisconnected('Remote end closed connection without response'))
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/opt/conda/lib/python3.11/site-packages/requests/adapters.py", line 486, in send
resp = conn.urlopen(
^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/urllib3/connectionpool.py", line 844, in urlopen
retries = retries.increment(
^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/urllib3/util/retry.py", line 515, in increment
raise MaxRetryError(_pool, url, reason) from reason # type: ignore[arg-type]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
urllib3.exceptions.MaxRetryError: HTTPConnectionPool(host='host.docker.internal', port=9000): Max retries exceeded with url: http://7702f0e1c7d4:8888/api (Caused by ProxyError('Unable to connect to proxy', RemoteDisconnected('Remote end closed connection without response')))
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/etc/jupyter/docker_healthcheck.py", line 19, in <module>
r = requests.get(url, verify=False) # request without SSL verification
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/requests/api.py", line 73, in get
return request("get", url, params=params, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/requests/sessions.py", line 589, in request
resp = self.send(prep, **send_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/requests/sessions.py", line 703, in send
r = adapter.send(request, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/requests/adapters.py", line 513, in send
raise ProxyError(e, request=request)
requests.exceptions.ProxyError: HTTPConnectionPool(host='host.docker.internal', port=9000): Max retries exceeded with url: http://7702f0e1c7d4:8888/api (Caused by ProxyError('Unable to connect to proxy', RemoteDisconnected('Remote end closed connection without response')))
```
### Anything else?
After investigating the issue further I came to the conclusion that using the proxy will be the problem. So I applied the following fix to ```docker_healthcheck.py```:
```python
proxies = {
"http": None,
"https": None,
}
r = requests.get(url, proxies=proxies, verify=False) # request without SSL verification
```
Now the healthcheck works!
### Latest Docker version
- [X] I've updated my Docker version to the latest available, and the issue still persists
| [
{
"content": "#!/usr/bin/env python3\n# Copyright (c) Jupyter Development Team.\n# Distributed under the terms of the Modified BSD License.\nimport json\nimport os\nfrom pathlib import Path\n\nimport requests\n\n# A number of operations below deliberately don't check for possible errors\n# As this is a healthch... | [
{
"content": "#!/usr/bin/env python3\n# Copyright (c) Jupyter Development Team.\n# Distributed under the terms of the Modified BSD License.\nimport json\nimport os\nfrom pathlib import Path\n\nimport requests\n\n# A number of operations below deliberately don't check for possible errors\n# As this is a healthch... | diff --git a/base-notebook/docker_healthcheck.py b/base-notebook/docker_healthcheck.py
index 7c35a6b115..41bbc78568 100755
--- a/base-notebook/docker_healthcheck.py
+++ b/base-notebook/docker_healthcheck.py
@@ -16,6 +16,11 @@
url = json.loads(json_file.read_bytes())["url"]
url = url + "api"
-r = requests.get(url, verify=False) # request without SSL verification
+proxies = {
+ "http": "",
+ "https": "",
+}
+
+r = requests.get(url, proxies=proxies, verify=False) # request without SSL verification
r.raise_for_status()
print(r.content)
diff --git a/tests/base-notebook/test_healthcheck.py b/tests/base-notebook/test_healthcheck.py
index 2b15faf605..73de100303 100644
--- a/tests/base-notebook/test_healthcheck.py
+++ b/tests/base-notebook/test_healthcheck.py
@@ -69,6 +69,53 @@ def test_health(
assert get_health(running_container) == "healthy"
+@pytest.mark.parametrize(
+ "env,cmd,user",
+ [
+ (
+ ["HTTPS_PROXY=host.docker.internal", "HTTP_PROXY=host.docker.internal"],
+ None,
+ None,
+ ),
+ (
+ [
+ "NB_USER=testuser",
+ "CHOWN_HOME=1",
+ "JUPYTER_PORT=8123",
+ "HTTPS_PROXY=host.docker.internal",
+ "HTTP_PROXY=host.docker.internal",
+ ],
+ ["start-notebook.sh", "--ServerApp.base_url=/test"],
+ "root",
+ ),
+ ],
+)
+def test_health_proxy(
+ container: TrackedContainer,
+ env: Optional[list[str]],
+ cmd: Optional[list[str]],
+ user: Optional[str],
+) -> None:
+ running_container = container.run_detached(
+ tty=True,
+ environment=env,
+ command=cmd,
+ user=user,
+ )
+
+ # sleeping some time to let the server start
+ time_spent = 0.0
+ wait_time = 0.1
+ time_limit = 15
+ while time_spent < time_limit:
+ time.sleep(wait_time)
+ time_spent += wait_time
+ if get_health(running_container) == "healthy":
+ return
+
+ assert get_health(running_container) == "healthy"
+
+
@pytest.mark.parametrize(
"env,cmd,user",
[
|
dj-stripe__dj-stripe-1312 | Issue when attempting to sync tiered Price Model in 2.4.2
**Describe the bug**
It looks like 9bd896ffd944e809b95abae884a2149dc8a79f27 introduced a regression when trying to sync a tiered Price model. Probably Price is not the only model affected.
Check out this trace:
```
$ ./manage.py djstripe_sync_models Price
Syncing Price:
INFO stripe.log_info:64- message='Request to Stripe api' method=get path=https://api.stripe.com/v1/prices?expand[0]=data.tiers
INFO stripe.log_info:64- message='Stripe API response' path=https://api.stripe.com/v1/prices?expand[0]=data.tiers response_code=200
id=price_1IFltoFz0jfFqjGsm5fbXWt5, pk=6 (xxx)
id=price_1IFe29Fz0jfFqjGsTpBrPQql, pk=1 (xxx)
id=price_1IFe29Fz0jfFqjGslZM7rvu1, pk=2 (xxx)
id=price_1IFe28Fz0jfFqjGsM0SIOAa6, pk=3 (xxx)
id=price_1IFe27Fz0jfFqjGsEN4c0MxR, pk=4 (xxx)
id=price_1IFe23Fz0jfFqjGsbFrlPDSi, pk=5 (xxx)
INFO stripe.log_info:64- message='Request to Stripe api' method=get path=https://api.stripe.com/v1/prices
INFO stripe.log_info:64- message='Stripe API response' path=https://api.stripe.com/v1/prices response_code=200
id=price_1IFltoFz0jfFqjGsm5fbXWt5, pk=6 (xxx)
id=price_1IFe29Fz0jfFqjGsTpBrPQql, pk=1 (xxx)
id=price_1IFe29Fz0jfFqjGslZM7rvu1, pk=2 (xxx)
id=price_1IFe28Fz0jfFqjGsM0SIOAa6, pk=3 (xxx)
id=price_1IFe27Fz0jfFqjGsEN4c0MxR, pk=4 (xxx)
id=price_1IFe23Fz0jfFqjGsbFrlPDSi, pk=5 (xxx)
Synced 12 Price
```
The Price objects are synced twice. The first time with the tiers attribute expanded and the second time without expanding it and overwriting it, so the final object doesn't include tiers.
**Software versions**
- dj-stripe version: 2.4.2
- Python version: 3.7
- Django version: 3.0.11
- Stripe API version: 2.55
- Database type and version: postgresql 10.10
**Steps To Reproduce**
1. Create tiered Price and add tiers in Stripe Dashboard
2. Sync Price models with manage command
**Can you reproduce the issue with the latest version of master?**
Yes, both 2.4.2 and master are affected (2.4.1 is not affected)
**Expected Behavior**
The Price Model should have the tiers JSONField object populated.
| [
{
"content": "from typing import List\n\nfrom django.apps import apps\nfrom django.core.management.base import BaseCommand, CommandError\n\nfrom ... import models, settings\n\n\nclass Command(BaseCommand):\n \"\"\"Sync models from stripe.\"\"\"\n\n help = \"Sync models from stripe.\"\n\n def add_argume... | [
{
"content": "from typing import List\n\nfrom django.apps import apps\nfrom django.core.management.base import BaseCommand, CommandError\n\nfrom ... import models, settings\n\n\nclass Command(BaseCommand):\n \"\"\"Sync models from stripe.\"\"\"\n\n help = \"Sync models from stripe.\"\n\n def add_argume... | diff --git a/djstripe/management/commands/djstripe_sync_models.py b/djstripe/management/commands/djstripe_sync_models.py
index ddea11aa16..a974055826 100644
--- a/djstripe/management/commands/djstripe_sync_models.py
+++ b/djstripe/management/commands/djstripe_sync_models.py
@@ -140,7 +140,7 @@ def get_list_kwargs(self, model):
for subscription in models.Subscription.api_list()
)
)
- else:
+ elif not all_list_kwargs:
all_list_kwargs.append({})
return all_list_kwargs
|
DDMAL__CantusDB-913 | Admin Chant Edit page: we should make "title" field longer
In an email from Debra:
> https://cantusdatabase.org/admin/main_app/source/123611/change/
For a page like this one, could I please have a longer box for the source title? The width of the "provenance note" field would be about right, I think, so that I can see the whole title without having to scroll.
| [
{
"content": "from django import forms\nfrom .models import (\n Chant,\n Office,\n Genre,\n Notation,\n Feast,\n Source,\n RismSiglum,\n Segment,\n Provenance,\n Century,\n Sequence,\n)\nfrom .widgets import (\n TextInputWidget,\n VolpianoInputWidget,\n TextAreaWidget,\... | [
{
"content": "from django import forms\nfrom .models import (\n Chant,\n Office,\n Genre,\n Notation,\n Feast,\n Source,\n RismSiglum,\n Segment,\n Provenance,\n Century,\n Sequence,\n)\nfrom .widgets import (\n TextInputWidget,\n VolpianoInputWidget,\n TextAreaWidget,\... | diff --git a/django/cantusdb_project/main_app/forms.py b/django/cantusdb_project/main_app/forms.py
index d595973c3..d106d4284 100644
--- a/django/cantusdb_project/main_app/forms.py
+++ b/django/cantusdb_project/main_app/forms.py
@@ -826,6 +826,7 @@ class Meta:
widget=AdminTextInputWidget,
help_text="Full Manuscript Identification (City, Archive, Shelf-mark)",
)
+ title.widget.attrs.update({"style": "width: 610px;"})
siglum = forms.CharField(
required=True,
|
webkom__lego-3128 | Broken link on weekly mails
The link used to unsubscribe from the mail is broken, because `frontend_url` is undefined. Probably due to the weekly mails being handled differently than all other notifications.
| [
{
"content": "from datetime import timedelta\n\nfrom django.conf import settings\nfrom django.template.loader import render_to_string\nfrom django.utils import timezone\n\nfrom premailer import transform\nfrom structlog import get_logger\n\nfrom lego import celery_app\nfrom lego.apps.events.constants import EVE... | [
{
"content": "from datetime import timedelta\n\nfrom django.conf import settings\nfrom django.template.loader import render_to_string\nfrom django.utils import timezone\n\nfrom premailer import transform\nfrom structlog import get_logger\n\nfrom lego import celery_app\nfrom lego.apps.events.constants import EVE... | diff --git a/lego/apps/email/tasks.py b/lego/apps/email/tasks.py
index a9b946bc7..3270a3e2b 100644
--- a/lego/apps/email/tasks.py
+++ b/lego/apps/email/tasks.py
@@ -93,6 +93,7 @@ def create_weekly_mail(user):
if todays_weekly is None
else todays_weekly.get_absolute_url(),
"joblistings": joblistings,
+ "frontend_url": settings.FRONTEND_URL,
},
)
if events or joblistings or todays_weekly:
|
OpenMined__PySyft-4923 | Small correction in Sample Code Block of sy.core.pointer.pointer
## Where?
Where are you looking to add documentation? Which file? Which feature?
In directory PySyft/src/syft/core/pointer directory, there is a python file, pointer.py.
The example code in the python file can be viewed by the user by executing the following command:
import syft as sy
sy.core.pointer.pointer?
The example code in the output of above command is as follows:
Example:
.. code-block::
# creating the data holder domain
domain_1 = Domain(name="Data holder domain")
# creating dummy data
tensor = th.tensor([1, 2, 3])
# creating the data holder client
domain_1_client = domain_1.get_root_client()
# sending the data to the client and receiving a pointer of that data.
data_ptr_domain_1 = tensor.send(domain_1_client) # or tensor.send_to(domain_1_client)
# creating the data user domain
domain_2 = Domain(name="Data user domain")
# creating a request to access the data
data_ptr_domain_1.request(
name="My Request", reason="I'd lke to see this pointer"
)
# getting the remote id of the object
requested_object = data_ptr_domain_1.id_at_location
message_request_id = domain_1_client.request_queue.get_request_id_from_object_id(
object_id=requested_object
)
# the data holder accepts the request
domain_1.requests[0].owner_client_if_available = domain_1_client
domain_1.requests[0].accept()
# the data user checks if the data holder approved his request
response = data_ptr_domain_1.check_access(node=domain_2, request_id=message_request_id)
**The following line is where the issue is:**
# getting the request id
message_request_id = domain_1_client.request_queue.get_request_id_from_object_id(
object_id=requested_object
)
**Error Generated by the line is:**
AttributeError: 'DomainClient' object has no attribute 'request_queue'
**Solution** :
Replacing "request_queue" to "requests" solves the issue.
| [
{
"content": "\"\"\"A Pointer is the main handler when interacting with remote data.\nA Pointer object represents an API for interacting with data (of any type)\nat a specific location. The pointer should never be instantiated, only subclassed.\n\nThe relation between pointers and data is many to one,\nthere ca... | [
{
"content": "\"\"\"A Pointer is the main handler when interacting with remote data.\nA Pointer object represents an API for interacting with data (of any type)\nat a specific location. The pointer should never be instantiated, only subclassed.\n\nThe relation between pointers and data is many to one,\nthere ca... | diff --git a/src/syft/core/pointer/pointer.py b/src/syft/core/pointer/pointer.py
index da8206d191d..6e94230b000 100644
--- a/src/syft/core/pointer/pointer.py
+++ b/src/syft/core/pointer/pointer.py
@@ -68,7 +68,7 @@
requested_object = data_ptr_domain_1.id_at_location
# getting the request id
- message_request_id = domain_1_client.request_queue.get_request_id_from_object_id(
+ message_request_id = domain_1_client.requests.get_request_id_from_object_id(
object_id=requested_object
)
|
cloud-custodian__cloud-custodian-2513 | S3Output should grant bucket owner full control
Currently `S3Output` misses the `ACL` option. In a cross account setup it is desirable to give the bucket owner full control.
Would you change the code like this?:
```
diff --git a/c7n/output.py b/c7n/output.py
index c3839c2f..5fb06f59 100644
--- a/c7n/output.py
+++ b/c7n/output.py
@@ -268,6 +268,7 @@ class S3Output(FSOutput):
self.transfer.upload_file(
os.path.join(root, f), self.bucket, key,
extra_args={
+ 'ACL': 'bucket-owner-full-control',
'ServerSideEncryption': 'AES256'})
def use_s3(self):
```
| [
{
"content": "# Copyright 2015-2017 Capital One Services, LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requi... | [
{
"content": "# Copyright 2015-2017 Capital One Services, LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requi... | diff --git a/c7n/output.py b/c7n/output.py
index 47109950cd7..406f40c2287 100644
--- a/c7n/output.py
+++ b/c7n/output.py
@@ -284,4 +284,5 @@ def upload(self):
self.transfer.upload_file(
os.path.join(root, f), self.bucket, key,
extra_args={
+ 'ACL': 'bucket-owner-full-control',
'ServerSideEncryption': 'AES256'})
diff --git a/tests/test_output.py b/tests/test_output.py
index be4436b8294..05a3238e391 100644
--- a/tests/test_output.py
+++ b/tests/test_output.py
@@ -125,7 +125,7 @@ def test_upload(self):
fh.name,
"cloud-custodian",
"policies/xyz/%s/foo.txt" % output.date_path,
- extra_args={"ServerSideEncryption": "AES256"},
+ extra_args={"ACL": "bucket-owner-full-control", "ServerSideEncryption": "AES256"},
)
def test_sans_prefix(self):
@@ -143,5 +143,5 @@ def test_sans_prefix(self):
fh.name,
"cloud-custodian",
"policies/xyz/%s/foo.txt" % output.date_path,
- extra_args={"ServerSideEncryption": "AES256"},
+ extra_args={"ACL": "bucket-owner-full-control", "ServerSideEncryption": "AES256"},
)
|
bokeh__bokeh-6954 | length_units has no effect for rays
# READ AND FOLLOW THESE INSTRUCTIONS CAREFULLY
*ISSUES THAT DO NOT CONTAIN NECESSARY INFORMATION MAY BE CLOSED, IMMEDIATELY*
The issue tracker is NOT the place for general support. For questions and
technical assistance, come ask the [Bokeh mailing list](https://groups.google.com/a/continuum.io/forum/#!forum/bokeh) or join the chat on [Gitter](https://gitter.im/bokeh/bokeh). For feature requests, please provide a detailed description or proposal of the new capability or behavior.
For defects or deficiencies, please provide ALL OF THE FOLLOWING:
#### ALL software version info (bokeh, python, notebook, OS, browser, any other relevant packages)
Using Bokeh 0.12.9 in Chrome 59.0 on Fedora 25
#### Description of expected behavior and the observed behavior
The ``length_units`` attribute to ``Figure.ray`` has no effect. All rays are plotted in ``data`` units. Furthermore, the documentation has conflicting messages claiming the length defaults to screen units while length_units defaults to data
```
length (DistanceSpec) –
The length to extend the ray. Note that this length defaults to screen units.
```
while
```
length_units (Enum ( SpatialUnits )) – (default: ‘data’)
```
#### Complete, minimal, self-contained example code that reproduces the issue
```python
from bokeh.plotting import figure, curdoc
fig = figure(width=500, height=500)
fig.ray(x=[0, 0], y=[0, 0], angle=[0, 0.5], length=[100, 100],
length_units='screen', color='red')
fig.ray(x=[0, 0], y=[0, 0], angle=[-0.5, -1.5], length=[100, 100],
length_units='data', color='blue')
curdoc().add_root(fig)
```
#### Stack traceback and/or browser JavaScript console output
No errors
#### Screenshots or screencasts of the bug in action
All rays are equal length at all zoom levels
| [
{
"content": "# -*- coding: utf-8 -*-\n''' Display a variety of visual shapes whose attributes can be associated\nwith data columns from ``ColumnDataSources``.\n\nThe full list of glyphs built into Bokeh is given below:\n\n* :class:`~bokeh.models.glyphs.AnnularWedge`\n* :class:`~bokeh.models.glyphs.Annulus`\n* ... | [
{
"content": "# -*- coding: utf-8 -*-\n''' Display a variety of visual shapes whose attributes can be associated\nwith data columns from ``ColumnDataSources``.\n\nThe full list of glyphs built into Bokeh is given below:\n\n* :class:`~bokeh.models.glyphs.AnnularWedge`\n* :class:`~bokeh.models.glyphs.Annulus`\n* ... | diff --git a/bokeh/models/glyphs.py b/bokeh/models/glyphs.py
index b0842d0d381..fc54d1a6e86 100644
--- a/bokeh/models/glyphs.py
+++ b/bokeh/models/glyphs.py
@@ -784,7 +784,7 @@ class Ray(XYGlyph):
length = DistanceSpec(help="""
The length to extend the ray. Note that this ``length`` defaults
- to screen units.
+ to data units (measured in the x-direction).
""")
line_props = Include(LineProps, use_prefix=False, help="""
diff --git a/bokehjs/src/coffee/models/glyphs/ray.coffee b/bokehjs/src/coffee/models/glyphs/ray.coffee
index 0394e5fa3a5..9bdbf8f1f21 100644
--- a/bokehjs/src/coffee/models/glyphs/ray.coffee
+++ b/bokehjs/src/coffee/models/glyphs/ray.coffee
@@ -4,7 +4,10 @@ import * as p from "core/properties"
export class RayView extends XYGlyphView
_map_data: () ->
- @slength = @sdist(@renderer.xscale, @_x, @_length)
+ if @model.properties.length.units == "data"
+ @slength = @sdist(@renderer.xscale, @_x, @_length)
+ else
+ @slength = @_length
_render: (ctx, indices, {sx, sy, slength, _angle}) ->
if @visuals.line.doit
diff --git a/bokehjs/test/models/glyphs/index.coffee b/bokehjs/test/models/glyphs/index.coffee
index dd777789a8d..95f9752297a 100644
--- a/bokehjs/test/models/glyphs/index.coffee
+++ b/bokehjs/test/models/glyphs/index.coffee
@@ -1,4 +1,5 @@
require "./image"
require "./image_rgba"
require "./image_url"
+require "./ray"
require "./rect"
diff --git a/bokehjs/test/models/glyphs/ray.coffee b/bokehjs/test/models/glyphs/ray.coffee
new file mode 100644
index 00000000000..e71597886f4
--- /dev/null
+++ b/bokehjs/test/models/glyphs/ray.coffee
@@ -0,0 +1,90 @@
+{expect} = require "chai"
+utils = require "../../utils"
+sinon = require "sinon"
+
+{create_glyph_view} = require("./glyph_utils")
+{Ray, RayView} = utils.require("models/glyphs/ray")
+{LinearScale} = utils.require("models/scales/linear_scale")
+{LogScale} = utils.require("models/scales/log_scale")
+{Range1d} = utils.require("models/ranges/range1d")
+
+describe "Ray", ->
+
+ describe "RayView", ->
+
+ afterEach ->
+ utils.unstub_canvas()
+
+ beforeEach ->
+ utils.stub_canvas()
+
+ @glyph = new Ray({
+ x: {field: "x"}
+ y: {field: "y"}
+ length: {value: 10}
+ })
+
+ @set_scales = (glyph_view, type="linear") ->
+ if type == "linear"
+ scale = new LinearScale({
+ source_range: new Range1d({start: 0, end: 100})
+ target_range: new Range1d({start: 0, end: 200})
+ })
+ else if type == "reverse"
+ scale = new LinearScale({
+ source_range: new Range1d({start: 0, end: 100})
+ target_range: new Range1d({start: 200, end: 0})
+ })
+ else if type == "log"
+ scale = new LogScale({
+ source_range: new Range1d({start: 1, end: 1000})
+ target_range: new Range1d({start: 0, end: 200})
+ })
+ glyph_view.renderer.xscale = scale
+ glyph_view.renderer.yscale = scale
+ glyph_view.renderer.plot_view.frame.xscales['default'] = scale
+ glyph_view.renderer.plot_view.frame.yscales['default'] = scale
+
+ it "`_map_data` should correctly map data if length units are 'data'", ->
+ for angle in [0,1,2,3]
+ data = {x: [1], y: [2], length: [10]}
+ glyph_view = create_glyph_view(@glyph, data)
+
+ glyph_view.model.properties.length.units = "data"
+
+ @set_scales(glyph_view)
+ glyph_view.map_data()
+ expect(glyph_view.slength).to.be.deep.equal([20])
+
+ it "`_map_data` should correctly map data if length units are 'screen'", ->
+ for angle in [0,1,2,3]
+ data = {x: [1], y: [2], angle: [angle], length: [10]}
+ glyph_view = create_glyph_view(@glyph, data)
+
+ glyph_view.model.properties.length.units = "screen"
+
+ @set_scales(glyph_view)
+ glyph_view.map_data()
+ expect(glyph_view.slength).to.be.deep.equal([10])
+
+ it "`_map_data` should correctly map data if length units are 'data' and scale is reversed", ->
+ for angle in [0,1,2,3]
+ data = {x: [1], y: [2], length: [10]}
+ glyph_view = create_glyph_view(@glyph, data)
+
+ glyph_view.model.properties.length.units = "data"
+
+ @set_scales(glyph_view, "reverse")
+ glyph_view.map_data()
+ expect(glyph_view.slength).to.be.deep.equal([20])
+
+ it "`_map_data` should correctly map data if length units are 'screen' and scale is reversed", ->
+ for angle in [0,1,2,3]
+ data = {x: [1], y: [2], angle: [angle], length: [10]}
+ glyph_view = create_glyph_view(@glyph, data)
+
+ glyph_view.model.properties.length.units = "screen"
+
+ @set_scales(glyph_view, "reverse")
+ glyph_view.map_data()
+ expect(glyph_view.slength).to.be.deep.equal([10])
|
esphome__esphome-docs-1148 | Update docs for new fan speed
## Description:
**Related issue (if applicable):** fixes https://github.com/esphome/issues/issues/1278
**Pull request in [esphome](https://github.com/esphome/esphome) with YAML changes (if applicable):** esphome/esphome#https://github.com/esphome/esphome/pull/1391
## Checklist:
- [ ] Branch: `next` is for changes and new documentation that will go public with the next ESPHome release. Fixes, changes and adjustments for the current release should be created against `current`.
- [ ] Link added in `/index.rst` when creating new documents for new components or cookbook.
| [
{
"content": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n#\n# esphome documentation build configuration file, created by\n# sphinx-quickstart on Mon Jan 22 21:44:07 2018.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration ... | [
{
"content": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n#\n# esphome documentation build configuration file, created by\n# sphinx-quickstart on Mon Jan 22 21:44:07 2018.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration ... | diff --git a/Doxygen b/Doxygen
index 5d6b6d9846..31fa5d55d9 100644
--- a/Doxygen
+++ b/Doxygen
@@ -38,7 +38,7 @@ PROJECT_NAME = "ESPHome"
# could be handy for archiving the generated documentation or if some version
# control system is used.
-PROJECT_NUMBER = 1.17.1
+PROJECT_NUMBER = 1.17.2
# Using the PROJECT_BRIEF tag one can provide an optional one line description
# for a project that appears at the top of each page and should give viewer a
diff --git a/Makefile b/Makefile
index 3cf5f3a7d3..06150d7ba3 100644
--- a/Makefile
+++ b/Makefile
@@ -1,5 +1,5 @@
ESPHOME_PATH = ../esphome
-ESPHOME_REF = v1.17.1
+ESPHOME_REF = v1.17.2
.PHONY: html html-strict cleanhtml deploy help webserver Makefile netlify netlify-api api netlify-dependencies svg2png copy-svg2png
diff --git a/_static/version b/_static/version
index 507266ba01..0e1f39b86c 100644
--- a/_static/version
+++ b/_static/version
@@ -1 +1 @@
-1.17.1
\ No newline at end of file
+1.17.2
\ No newline at end of file
diff --git a/changelog/v1.17.0.rst b/changelog/v1.17.0.rst
index fddac3d815..d9ccf51782 100644
--- a/changelog/v1.17.0.rst
+++ b/changelog/v1.17.0.rst
@@ -79,6 +79,13 @@ Release 1.17.1 - May 5
- docs: Fixed typo in 1.17.0 changelogs :docspr:`1132` by :ghuser:`spacegaier`
- esphome: Do not call component update on failed components :esphomepr:`1392` by :ghuser:`alexyao2015`
+Release 1.17.2 - May 9
+----------------------
+
+- esphome: fixes #858 - esphome crashes with neolightbus and RMT :esphomepr:`1667` by :ghuser:`angelnu`
+- docs: Fix abundant apostrophes :docspr:`1137` by :ghuser:`jmartens`
+- docs: Add output part to binary light example :docspr:`1061` by :ghuser:`klaasnicolaas`
+
All changes
-----------
diff --git a/conf.py b/conf.py
index 993bcec6dc..f088451878 100644
--- a/conf.py
+++ b/conf.py
@@ -69,7 +69,7 @@
# The short X.Y version.
version = "1.17"
# The full version, including alpha/beta/rc tags.
-release = "1.17.1"
+release = "1.17.2"
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
diff --git a/guides/supporters.rst b/guides/supporters.rst
index 427837f9d3..cc302280d8 100644
--- a/guides/supporters.rst
+++ b/guides/supporters.rst
@@ -129,6 +129,7 @@ Contributors
- `Marcos Pérez Ferro (@djwmarcx) <https://github.com/djwmarcx>`__
- `Dan Mannock (@dmannock) <https://github.com/dmannock>`__
- `dmkif (@dmkif) <https://github.com/dmkif>`__
+- `Farzad E. (@dnetguru) <https://github.com/dnetguru>`__
- `DrZoid (@docteurzoidberg) <https://github.com/docteurzoidberg>`__
- `Jiang Sheng (@doskoi) <https://github.com/doskoi>`__
- `Robert Schütz (@dotlambda) <https://github.com/dotlambda>`__
@@ -528,4 +529,4 @@ Contributors
- `San (@zhujunsan) <https://github.com/zhujunsan>`__
- `Christian Zufferey (@zuzu59) <https://github.com/zuzu59>`__
-*This page was last updated May 5, 2021.*
+*This page was last updated May 9, 2021.*
|
microsoft__nni-5155 | Unclear what extras to install: `import nni.retiarii.execution.api` fails due to missing `pytorch_lightning`
**Describe the issue**:
I want to use `nni.retiarii.execution.api` module. I've installed it as below:
```
Collecting nni>=2.3
Downloading nni-2.9-py3-none-manylinux1_x86_64.whl (56.0 MB)
```
**Environment**:
- NNI version: 2.9
- Python version: 3.8
**Log message**:
```
_________________ ERROR collecting test/3rd_party/test_nni.py __________________
ImportError while importing test module '/__w/ai4cl-tianshou/ai4cl-tianshou/test/3rd_party/test_nni.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/local/lib/python3.8/importlib/__init__.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
test/3rd_party/test_nni.py:8: in <module>
import nni.retiarii.execution.api
/usr/local/lib/python3.8/site-packages/nni/retiarii/__init__.py:4: in <module>
from .operation import Operation
/usr/local/lib/python3.8/site-packages/nni/retiarii/operation.py:6: in <module>
from nni.nas.execution.common.graph_op import *
/usr/local/lib/python3.8/site-packages/nni/nas/__init__.py:4: in <module>
from .execution import *
/usr/local/lib/python3.8/site-packages/nni/nas/execution/__init__.py:4: in <module>
from .api import *
/usr/local/lib/python3.8/site-packages/nni/nas/execution/api.py:9: in <module>
from nni.nas.execution.common import (
/usr/local/lib/python3.8/site-packages/nni/nas/execution/common/__init__.py:4: in <module>
from .engine import *
/usr/local/lib/python3.8/site-packages/nni/nas/execution/common/engine.py:7: in <module>
from .graph import Model, MetricData
/usr/local/lib/python3.8/site-packages/nni/nas/execution/common/graph.py:18: in <module>
from nni.nas.evaluator import Evaluator
/usr/local/lib/python3.8/site-packages/nni/nas/evaluator/__init__.py:9: in <module>
shortcut_framework(__name__)
/usr/local/lib/python3.8/site-packages/nni/common/framework.py:93: in shortcut_framework
shortcut_module(current, '.' + get_default_framework(), current)
/usr/local/lib/python3.8/site-packages/nni/common/framework.py:83: in shortcut_module
mod = importlib.import_module(target, package)
/usr/local/lib/python3.8/importlib/__init__.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
/usr/local/lib/python3.8/site-packages/nni/nas/evaluator/pytorch/__init__.py:4: in <module>
from .lightning import *
/usr/local/lib/python3.8/site-packages/nni/nas/evaluator/pytorch/lightning.py:10: in <module>
import pytorch_lightning as pl
E ModuleNotFoundError: No module named 'pytorch_lightning'
```
**How to reproduce it?**:
```
pip install nni==2.9
python -c "import nni.retiarii.execution.api"
```
| [
{
"content": "# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nfrom .lightning import *\n",
"path": "nni/nas/evaluator/pytorch/__init__.py"
}
] | [
{
"content": "# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport warnings\n\ntry:\n from .lightning import *\nexcept ImportError:\n warnings.warn(\"PyTorch-Lightning must be installed to use PyTorch in NAS. \"\n \"If you are not using PyTorch, please `nni.... | diff --git a/nni/nas/evaluator/pytorch/__init__.py b/nni/nas/evaluator/pytorch/__init__.py
index 1b2a52c886..2a7b5d2a1b 100644
--- a/nni/nas/evaluator/pytorch/__init__.py
+++ b/nni/nas/evaluator/pytorch/__init__.py
@@ -1,4 +1,11 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
-from .lightning import *
+import warnings
+
+try:
+ from .lightning import *
+except ImportError:
+ warnings.warn("PyTorch-Lightning must be installed to use PyTorch in NAS. "
+ "If you are not using PyTorch, please `nni.set_default_framework('none')`")
+ raise
|
dotkom__onlineweb4-1524 | Hide irrelevant information if not applicable to the event
The event dashboard should provide useful information to the administrators of the events, and I think it should refrain from displaying information that's not relevant.
For example, all events include a column about "Extras" for the event, even though the event has no extras assigned. The column for "Paid" could be stripped if the event does not have a payment relation. If it's not an attendance event, the column for "showed up" could be removed.
This way we could add more information for events where this would be useful, without the table being too cramped.
| [
{
"content": "# -*- coding: utf-8 -*-\n\nfrom collections import OrderedDict\nfrom datetime import datetime, timedelta\nfrom functools import reduce\n\nfrom django.conf import settings\nfrom django.contrib.auth.models import Group\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.db impor... | [
{
"content": "# -*- coding: utf-8 -*-\n\nfrom collections import OrderedDict\nfrom datetime import datetime, timedelta\nfrom functools import reduce\n\nfrom django.conf import settings\nfrom django.contrib.auth.models import Group\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.db impor... | diff --git a/apps/events/models.py b/apps/events/models.py
index feead4710..93f2fed4f 100644
--- a/apps/events/models.py
+++ b/apps/events/models.py
@@ -375,6 +375,10 @@ def has_reservation(self):
except Reservation.DoesNotExist:
return False
+ @property
+ def has_extras(self):
+ return bool(self.extras.exists())
+
@property
def attendees_qs(self):
""" Queryset with all attendees not on waiting list """
diff --git a/templates/events/dashboard/details/attendees.html b/templates/events/dashboard/details/attendees.html
index 8dbf777ad..1eac4fbd9 100644
--- a/templates/events/dashboard/details/attendees.html
+++ b/templates/events/dashboard/details/attendees.html
@@ -24,9 +24,13 @@ <h3 class="panel-title">Påmeldte (<span id="attendees-count">{{ event.attendanc
<th>#</th>
<th>Fornavn</th>
<th>Etternavn</th>
+ {% if event.attendance_event.payment %}
<th>Betalt</th>
+ {% endif %}
<th>Møtt</th>
+ {% if event.attendance_event.has_extras %}
<th>Extra</th>
+ {% endif %}
<th>Fjern</th>
</tr>
</thead>
@@ -36,22 +40,26 @@ <h3 class="panel-title">Påmeldte (<span id="attendees-count">{{ event.attendanc
<td>{{ forloop.counter }}</td>
<td><a href="{% url 'dashboard_attendee_details' attendee.id %}">{{ attendee.user.first_name }}</a></td>
<td><a href="{% url 'dashboard_attendee_details' attendee.id %}">{{ attendee.user.last_name }}</a></td>
+ {% if event.attendance_event.payment %}
<td>
<a href="#" data-id="{{ attendee.id }}" class="toggle-attendee paid">
<i class="fa fa-lg {% if attendee.paid %}fa-check-square-o checked{% else %}fa-square-o{% endif %}"></i>
</a>
</td>
+ {% endif %}
<td>
<a href="#" data-id="{{ attendee.id }}" class="toggle-attendee attended">
<i class="fa fa-lg {% if attendee.attended %}fa-check-square-o checked{% else %}fa-square-o{% endif %}"></i>
</a>
</td>
+ {% if event.attendance_event.has_extras %}
<td>
{% if attendee.extras %}{{ attendee.extras }}{% else %}-{% endif %}
</td>
+ {% endif %}
<td>
<a href="#modal-delete-attendee" data-toggle="modal" data-id="{{ attendee.id }}" data-name="{{ attendee.user.get_full_name }}" class="remove-user">
- <i class="fa fa-times fa-lg pull-right red"></i>
+ <i class="fa fa-times fa-lg red"></i>
</a>
</td>
</tr>
@@ -81,9 +89,13 @@ <h3 class="panel-title">Venteliste (<span id="waitlist-count">{{ event.attendanc
<th>#</th>
<th>Fornavn</th>
<th>Etternavn</th>
+ {% if event.attendance_event.payment %}
<th>Betalt</th>
+ {% endif %}
<th>Møtt</th>
+ {% if event.attendance_event.has_extras %}
<th>Extra</th>
+ {% endif %}
<th>Fjern</th>
</tr>
</thead>
@@ -93,22 +105,26 @@ <h3 class="panel-title">Venteliste (<span id="waitlist-count">{{ event.attendanc
<td>{{ forloop.counter }}</td>
<td><a href="{% url 'dashboard_attendee_details' attendee.id %}">{{ attendee.user.first_name }}</a></td>
<td><a href="{% url 'dashboard_attendee_details' attendee.id %}">{{ attendee.user.last_name }}</a></td>
+ {% if event.attendance_event.payment %}
<td>
<a href="#" data-id="{{ attendee.id }}" class="toggle-attendee paid">
<i class="fa fa-lg {% if attendee.paid %}fa-check-square-o checked{% else %}fa-square-o{% endif %}"></i>
</a>
</td>
+ {% endif %}
<td>
<a href="#" data-id="{{ attendee.id }}" class="toggle-attendee attended">
<i class="fa fa-lg {% if attendee.attended %}fa-check-square-o checked{% else %}fa-square-o{% endif %}"></i>
</a>
</td>
+ {% if event.attendance_event.has_extras %}
<td>
{% if attendee.extras %}{{ attendee.extras }}{% else %}-{% endif %}
</td>
+ {% endif %}
<td>
<a href="#modal-delete-attendee" data-toggle="modal" data-id="{{ attendee.id }}" data-name="{{ attendee.user.get_full_name }}" class="remove-user">
- <i class="fa fa-times fa-lg pull-right red"></i>
+ <i class="fa fa-times fa-lg red"></i>
</a>
</td>
</tr>
@@ -127,7 +143,7 @@ <h3 class="panel-title">Venteliste (<span id="waitlist-count">{{ event.attendanc
<br />
<!-- extras -->
-{% if event.attendance_event.extras %}
+{% if event.attendance_event.has_extras %}
<div class="row">
<div class="col-lg-12">
<div class="panel panel-default">
|
nipy__nipype-3455 | matplotlib "normed" argument deprecated
### Summary
When trying to use the nipype.algorithms.metrics.Distance with method='eucl_mean', get the following error
`AttributeError: 'Rectangle' object has no property 'normed'`
My two input volumes are z-stat map outputs from fitlins of the same individual (both are in same shape/dimensions).
It seems like the normed argument is deprecated in matplotlib.
### Actual behavior
The following error
```
~/miniconda3/envs/fmri_analysis/lib/python3.9/site-packages/nipype/interfaces/base/core.py in run(self, cwd, ignore_exception, **inputs)
426 try:
427 runtime = self._pre_run_hook(runtime)
--> 428 runtime = self._run_interface(runtime)
429 runtime = self._post_run_hook(runtime)
430 outputs = self.aggregate_outputs(runtime)
~/miniconda3/envs/fmri_analysis/lib/python3.9/site-packages/nipype/algorithms/metrics.py in _run_interface(self, runtime)
200
201 elif self.inputs.method == "eucl_mean":
--> 202 self._distance = self._eucl_mean(nii1, nii2)
203
204 elif self.inputs.method == "eucl_wmean":
~/miniconda3/envs/fmri_analysis/lib/python3.9/site-packages/nipype/algorithms/metrics.py in _eucl_mean(self, nii1, nii2, weighted)
151
152 plt.figure()
--> 153 plt.hist(min_dist_matrix, 50, normed=1, facecolor="green")
154 plt.savefig(self._hist_filename)
155 plt.clf()
~/miniconda3/envs/fmri_analysis/lib/python3.9/site-packages/matplotlib/pyplot.py in hist(x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, data, **kwargs)
2683 orientation='vertical', rwidth=None, log=False, color=None,
2684 label=None, stacked=False, *, data=None, **kwargs):
-> 2685 return gca().hist(
2686 x, bins=bins, range=range, density=density, weights=weights,
2687 cumulative=cumulative, bottom=bottom, histtype=histtype,
~/miniconda3/envs/fmri_analysis/lib/python3.9/site-packages/matplotlib/__init__.py in inner(ax, data, *args, **kwargs)
1445 def inner(ax, *args, data=None, **kwargs):
1446 if data is None:
-> 1447 return func(ax, *map(sanitize_sequence, args), **kwargs)
1448
1449 bound = new_sig.bind(ax, *args, **kwargs)
~/miniconda3/envs/fmri_analysis/lib/python3.9/site-packages/matplotlib/axes/_axes.py in hist(self, x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)
6813 if patch:
6814 p = patch[0]
-> 6815 p.update(kwargs)
6816 if lbl is not None:
6817 p.set_label(lbl)
~/miniconda3/envs/fmri_analysis/lib/python3.9/site-packages/matplotlib/artist.py in update(self, props)
994 func = getattr(self, f"set_{k}", None)
995 if not callable(func):
--> 996 raise AttributeError(f"{type(self).__name__!r} object "
997 f"has no property {k!r}")
998 ret.append(func(v))
AttributeError: 'Rectangle' object has no property 'normed'
```
### Expected behavior
Histogram output
### Platform details:
<!-- Please run the following code from your shell and place the output between the triple ticks, below.
python -c "import nipype; from pprint import pprint; pprint(nipype.get_info())"
-->
```
{'commit_hash': '0289137',
'commit_source': 'installation',
'networkx_version': '2.5.1',
'nibabel_version': '2.5.1',
'nipype_version': '1.6.1',
'numpy_version': '1.20.3',
'pkg_path': '/home/users/sjshim/miniconda3/envs/fmri_analysis/lib/python3.9/site-packages/nipype',
'scipy_version': '1.5.3',
'sys_executable': '/home/users/sjshim/miniconda3/envs/fmri_analysis/bin/python',
'sys_platform': 'linux',
'sys_version': '3.9.7 (default, Sep 16 2021, 13:09:58) \n[GCC 7.5.0]',
'traits_version': '6.2.0'}
```
| [
{
"content": "# -*- coding: utf-8 -*-\n# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n\"\"\"\nImage assessment algorithms. Typical overlap and error computation\nmeasures to evaluate results from other processing units.\n\"\"\"\nimport os\nimp... | [
{
"content": "# -*- coding: utf-8 -*-\n# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n\"\"\"\nImage assessment algorithms. Typical overlap and error computation\nmeasures to evaluate results from other processing units.\n\"\"\"\nimport os\nimp... | diff --git a/nipype/algorithms/metrics.py b/nipype/algorithms/metrics.py
index fc209a9d27..b58e7fc59b 100644
--- a/nipype/algorithms/metrics.py
+++ b/nipype/algorithms/metrics.py
@@ -150,7 +150,7 @@ def _eucl_mean(self, nii1, nii2, weighted=False):
import matplotlib.pyplot as plt
plt.figure()
- plt.hist(min_dist_matrix, 50, normed=1, facecolor="green")
+ plt.hist(min_dist_matrix, 50, density=True, facecolor="green")
plt.savefig(self._hist_filename)
plt.clf()
plt.close()
|
privacyidea__privacyidea-3786 | %' is not properly decoded by the Privacyidea server
Using credential provider 3.4.0
Using Privacyidea 3.8 or 3.9
Ticket 3554
When there is a % in the password of the user, Authentication failed.
If the % is at the beginning or the end of the password, the authentication is successful.
The issue on the side of the credential provider is fixed since the 3.4
https://github.com/privacyidea/privacyidea-credential-provider/releases/tag/v3.4.0
_Fixed a bug where the '%' was not properly encoded when communicating with Privacyidea.
So it must be the decoding of the password on the side of the server that goes wrong
| [
{
"content": "# -*- coding: utf-8 -*-\n# privacyIDEA is a fork of LinOTP\n# May 08, 2014 Cornelius Kölbel\n# License: AGPLv3\n# contact: http://www.privacyidea.org\n#\n# Copyright (C) 2010 - 2014 LSE Leading Security Experts GmbH\n# License: AGPLv3\n# contact: http://www.linotp.org\n# http... | [
{
"content": "# -*- coding: utf-8 -*-\n# privacyIDEA is a fork of LinOTP\n# May 08, 2014 Cornelius Kölbel\n# License: AGPLv3\n# contact: http://www.privacyidea.org\n#\n# Copyright (C) 2010 - 2014 LSE Leading Security Experts GmbH\n# License: AGPLv3\n# contact: http://www.linotp.org\n# http... | diff --git a/privacyidea/api/lib/utils.py b/privacyidea/api/lib/utils.py
index e11d45ad02..4043c81909 100644
--- a/privacyidea/api/lib/utils.py
+++ b/privacyidea/api/lib/utils.py
@@ -51,7 +51,8 @@
# TODO: we should probably switch this when we do not do the extra unquote anymore
NO_UNQUOTE_USER_AGENTS = {
'privacyIDEA-LDAP-Proxy': None,
- 'simpleSAMLphp': None
+ 'simpleSAMLphp': None,
+ 'privacyidea-cp': None
}
SESSION_KEY_LENGTH = 32
diff --git a/tests/test_api_lib_utils.py b/tests/test_api_lib_utils.py
index 674b5dd4c9..416aa45c78 100644
--- a/tests/test_api_lib_utils.py
+++ b/tests/test_api_lib_utils.py
@@ -305,6 +305,15 @@ def test_09_check_unquote(self):
res = self.app.full_dispatch_request()
self.assertTrue(res.status_code == 200, res)
+ # And check the Credential Provider plugin
+ with self.app.test_request_context('/auth',
+ method='POST',
+ data={'username': 'pwpercent',
+ 'password': 'pw%45#test'},
+ headers={'User-Agent': 'privacyidea-cp/2.0'}):
+ res = self.app.full_dispatch_request()
+ self.assertTrue(res.status_code == 200, res)
+
# now check the /validate/check endpoint
set_policy(name="otppin",
scope=SCOPE.AUTH,
@@ -346,6 +355,18 @@ def test_09_check_unquote(self):
result = res.json.get("result")
self.assertTrue(result.get("value"), res.json)
+ # when using the correct user-agent (CredentialProvider), quoting is not necessary
+ with self.app.test_request_context('/validate/check',
+ method='POST',
+ data={"user": "pwpercent",
+ "realm": self.realm1,
+ "pass": "pw%45#test"},
+ headers={'User-Agent': 'privacyidea-cp/3.0'}):
+ res = self.app.full_dispatch_request()
+ self.assertEqual(res.status_code, 200, res)
+ result = res.json.get("result")
+ self.assertTrue(result.get("value"), res.json)
+
# cleanup
remove_token(serial='spass1d')
delete_policy('otppin')
|
dynaconf__dynaconf-953 | [bug] reload() function does not clear old hooks
**Describe the bug**
the `reload()` function of the `LazySettings` class does not clear the `_loaded_hooks` attribute. Because of that, when trying to reload and rerun the loaders, the hook won't run again.
**To Reproduce**
Steps to reproduce the behavior:
Configure a post hook just like in the docs. Create a `DynaConf` object and then run `reload()` on the object.
**Expected behavior**
The post hook should run after every reload.
**Additional context**
I'll be glad to make a PR for this myself.
| [
{
"content": "from __future__ import annotations\n\nimport copy\nimport glob\nimport importlib\nimport inspect\nimport os\nimport warnings\nfrom collections import defaultdict\nfrom contextlib import contextmanager\nfrom contextlib import suppress\nfrom pathlib import Path\nfrom typing import Any\nfrom typing i... | [
{
"content": "from __future__ import annotations\n\nimport copy\nimport glob\nimport importlib\nimport inspect\nimport os\nimport warnings\nfrom collections import defaultdict\nfrom contextlib import contextmanager\nfrom contextlib import suppress\nfrom pathlib import Path\nfrom typing import Any\nfrom typing i... | diff --git a/dynaconf/base.py b/dynaconf/base.py
index 355760a9d..39cffe9ad 100644
--- a/dynaconf/base.py
+++ b/dynaconf/base.py
@@ -1124,6 +1124,7 @@ def loaders(self): # pragma: no cover
def reload(self, env=None, silent=None): # pragma: no cover
"""Clean end Execute all loaders"""
self.clean()
+ self._loaded_hooks.clear()
self.execute_loaders(env, silent)
def execute_loaders(
diff --git a/tests_functional/issues/850_reload-hooks/app.py b/tests_functional/issues/850_reload-hooks/app.py
new file mode 100644
index 000000000..884854956
--- /dev/null
+++ b/tests_functional/issues/850_reload-hooks/app.py
@@ -0,0 +1,12 @@
+"""
+Assert settings.reload() re-reruns hooks.
+"""
+from __future__ import annotations
+
+from dynaconf import Dynaconf
+
+settings = Dynaconf(settings_file="settings.toml")
+
+assert settings.counter == 1
+settings.reload()
+assert settings.counter == 1
diff --git a/tests_functional/issues/850_reload-hooks/dynaconf_hooks.py b/tests_functional/issues/850_reload-hooks/dynaconf_hooks.py
new file mode 100644
index 000000000..e07f2df85
--- /dev/null
+++ b/tests_functional/issues/850_reload-hooks/dynaconf_hooks.py
@@ -0,0 +1,5 @@
+from __future__ import annotations
+
+
+def post(settings):
+ return {"counter": settings.counter + 1}
diff --git a/tests_functional/issues/850_reload-hooks/settings.toml b/tests_functional/issues/850_reload-hooks/settings.toml
new file mode 100644
index 000000000..343a3f3e7
--- /dev/null
+++ b/tests_functional/issues/850_reload-hooks/settings.toml
@@ -0,0 +1 @@
+counter=0
|
pytorch__rl-1910 | [BUG] I had to patch these 2 methods in order to run my script
## Describe the bug
(1)
`DoubleToFloat` transform cannot transform to `float` if env to be transformed is already a `float` (e.g. it is a `float` and the input dtype was declared as `double`).
I mean, all it has to do is call `.to(torch.float)` or something, which can be called regardless of the `dtype` of the input tensor, so why not?
(2)
The `torchrl.objectives.utils._cache_values` function is also bugged I think. If there is no `_cache` attribute in `self.__dict__`, it raises an error, but the function seems to be implemented in a way that it can work anyway. In fact, when I manually do `self.__dict__["_cache"] = {}`, all is good.
This happens with the `torchrl.objectives.ClipPPOLoss`.
## To Reproduce
I do not know how to reproduce, although my project is fully available at: https://gitlab.com/svnv-svsv-jm/chess-rl/-/tree/develop?ref_type=heads
For more context, I am trying to combine `torchrl` with `pytorch-lightning`.
## Reason and Possible fixes
For the cache issue with `torchrl.objectives.ClipPPOLoss`, I patched this:
```python
# in torchrl.objectives.utils
def _cache_values(fun):
"""Caches the tensordict returned by a property."""
name = fun.__name__
def new_fun(self, netname=None):
__dict__ = self.__dict__
# MY PATCH START
try:
_cache = __dict__["_cache"]
except KeyError as ex:
__dict__["_cache"] = {}
_cache = __dict__["_cache"]
# MY PATCH END
attr_name = name
if netname is not None:
attr_name += "_" + netname
if attr_name in _cache:
out = _cache[attr_name]
return out
if netname is not None:
out = fun(self, netname)
else:
out = fun(self)
# TODO: decide what to do with locked tds in functional calls
# if is_tensor_collection(out):
# out.lock_()
_cache[attr_name] = out
return out
return new_fun
```
For the `torch.envs.DoubleToFloat`, I patched `DTypeCastTransform`:
```python
# class DTypeCastTransform
def transform_observation_spec(self, observation_spec):
full_observation_spec = observation_spec
for observation_key, observation_spec in list(
full_observation_spec.items(True, True)
):
# find out_key that match the in_key
for in_key, out_key in zip(self.in_keys, self.out_keys):
if observation_key == in_key:
if observation_spec.dtype != self.dtype_in:
# PATCH START
self.dtype_in = observation_spec.dtype
# raise TypeError(
# f"observation_spec.dtype is not {self.dtype_in}"
# )
# PATCH END
full_observation_spec[out_key] = self._transform_spec(
observation_spec
)
return full_observation_spec
```
After these patches, all runs smoothly again.
Sorry for not providing a minimal example, but I really wouldn't know how to create one from my code. These are the tests where I encountered the errors: https://gitlab.com/svnv-svsv-jm/chess-rl/-/tree/develop/tests/shark/models/ppo?ref_type=heads
| [
{
"content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport functools\nimport re\nimport warnings\nfrom enum import Enum\nfrom typing import Iterable, Optional, Union... | [
{
"content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport functools\nimport re\nimport warnings\nfrom enum import Enum\nfrom typing import Iterable, Optional, Union... | diff --git a/torchrl/objectives/utils.py b/torchrl/objectives/utils.py
index b234af6a804..9afbf8095f0 100644
--- a/torchrl/objectives/utils.py
+++ b/torchrl/objectives/utils.py
@@ -459,7 +459,7 @@ def _cache_values(fun):
def new_fun(self, netname=None):
__dict__ = self.__dict__
- _cache = __dict__["_cache"]
+ _cache = __dict__.setdefault("_cache", {})
attr_name = name
if netname is not None:
attr_name += "_" + netname
|
techmatters__terraso-backend-81 | Add photo field to the User model
## Description
The user profile photo might be automatically fetched from the third-party account system (Google or Apple), or it can also be uploaded from by the user. Since the file itself might be stored on an external storage service, this field will be used to store the location of the file.
In this issue, it's important to consider the flow front-end → back-end for photo upload.
## Suggested subtasks
- [ ] Design the overall flow to upload photo considering front-end → back-end flow
- [ ] Add the new field on model with proper support to the external storage service (upload) and update DB migrations
- [ ] Implement upload feature to update photo
- [ ] Add support to present the proper photo URL from external services
- [ ] Add the new photo field on user API
This issue depends on:
- #21
| [
{
"content": "import graphene\nfrom graphene import relay\nfrom graphene_django import DjangoObjectType\n\nfrom apps.core.models import User\n\nfrom .commons import BaseDeleteMutation\n\n\nclass UserNode(DjangoObjectType):\n id = graphene.ID(source=\"pk\", required=True)\n\n class Meta:\n model = U... | [
{
"content": "import graphene\nfrom graphene import relay\nfrom graphene_django import DjangoObjectType\n\nfrom apps.core.models import User\n\nfrom .commons import BaseDeleteMutation\n\n\nclass UserNode(DjangoObjectType):\n id = graphene.ID(source=\"pk\", required=True)\n\n class Meta:\n model = U... | diff --git a/terraso_backend/apps/graphql/schema/users.py b/terraso_backend/apps/graphql/schema/users.py
index f8c8c8b88..9018ff1bf 100644
--- a/terraso_backend/apps/graphql/schema/users.py
+++ b/terraso_backend/apps/graphql/schema/users.py
@@ -17,7 +17,7 @@ class Meta:
"first_name": ["icontains"],
"last_name": ["icontains"],
}
- fields = ("email", "first_name", "last_name", "memberships")
+ fields = ("email", "first_name", "last_name", "profile_image", "memberships")
interfaces = (relay.Node,)
diff --git a/terraso_backend/tests/graphql/test_core_request.py b/terraso_backend/tests/graphql/test_core_request.py
index 56204c23d..deba83f96 100644
--- a/terraso_backend/tests/graphql/test_core_request.py
+++ b/terraso_backend/tests/graphql/test_core_request.py
@@ -144,6 +144,7 @@ def test_users_query(client_query, users):
edges {
node {
email
+ profileImage
}
}
}
@@ -151,6 +152,8 @@ def test_users_query(client_query, users):
"""
)
edges = response.json()["data"]["users"]["edges"]
- users_result = [edge["node"]["email"] for edge in edges]
+ users_result_nodes = [edge["node"] for edge in edges]
for user in users:
- assert user.email in users_result
+ user_node = next(item for item in users_result_nodes if item["email"] == user.email)
+ assert user_node
+ assert user.profile_image == user_node["profileImage"]
|
cowrie__cowrie-920 | output_localsyslog exceptions.KeyError: 'isError'
After pulling the most recent version of cowrie to some of my honeypots, I get this error when a new connection I enabled [output_localsyslog] with configuration below:
```
[output_localsyslog]
enabled = true
facility = LOCAL5
format = text
```
The log error shows this:
```
2018-10-11T18:29:01.778300+0000 [twisted.logger._observer#critical] Temporarily disabling observer LegacyLogObserverWrapper(<bound method Output.emit of <cowrie.output.localsyslog.Output object at 0xb55ae7b0>>) due to exception: [Failure instance: Traceback: <type 'exceptions.KeyError'>: 'isError'
/opt/cowrie/src/cowrie/core/checkers.py:110:checkUserPass
/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/python/threadable.py:53:sync
/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/python/log.py:286:msg
/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/logger/_legacy.py:154:publishToNewObserver
--- <exception caught here> ---
/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/logger/_observer.py:131:__call__
/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/logger/_legacy.py:93:__call__
/opt/cowrie/src/cowrie/core/output.py:209:emit
/opt/cowrie/src/cowrie/output/localsyslog.py:65:write
/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/python/syslog.py:76:emit
]
Traceback (most recent call last):
File "/opt/cowrie/src/cowrie/core/checkers.py", line 110, in checkUserPass
password=thepassword)
File "/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/python/threadable.py", line 53, in sync
return function(self, *args, **kwargs)
File "/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/python/log.py", line 286, in msg
_publishNew(self._publishPublisher, actualEventDict, textFromEventDict)
File "/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/logger/_legacy.py", line 154, in publishToNewObserver
observer(eventDict)
--- <exception caught here> ---
File "/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/logger/_observer.py", line 131, in __call__
observer(event)
File "/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/logger/_legacy.py", line 93, in __call__
self.legacyObserver(event)
File "/opt/cowrie/src/cowrie/core/output.py", line 209, in emit
self.write(ev)
File "/opt/cowrie/src/cowrie/output/localsyslog.py", line 65, in write
self.syslog.emit(logentry)
File "/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/python/syslog.py", line 76, in emit
if eventDict['isError']:
exceptions.KeyError: 'isError'
```
| [
{
"content": "# Copyright (c) 2015 Michel Oosterhof <michel@oosterhof.net>\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions\n# are met:\n#\n# 1. Redistributions of source code must retain the ab... | [
{
"content": "# Copyright (c) 2015 Michel Oosterhof <michel@oosterhof.net>\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions\n# are met:\n#\n# 1. Redistributions of source code must retain the ab... | diff --git a/src/cowrie/output/localsyslog.py b/src/cowrie/output/localsyslog.py
index 751e8d835c..1156a82d30 100644
--- a/src/cowrie/output/localsyslog.py
+++ b/src/cowrie/output/localsyslog.py
@@ -53,6 +53,9 @@ def stop(self):
pass
def write(self, logentry):
+ if 'isError' not in logentry:
+ logentry['isError'] = False
+
if self.format == 'cef':
self.syslog.emit({
'message': cowrie.core.cef.formatCef(logentry),
|
scikit-image__scikit-image-6343 | imageIO warnings due to v2 -> v3 migration
## Description
As of imageIO 2.16.0 (Feb22) there are now a v2 and v3 namespaces in addition to the top-level namespace. As of 2.16.2 (released Apr22) directly using the top-level namespace results in warnings to either explicitly opt-into the v3 API or opt-out and import the v2.
This in turn causes warnings when using `skimage.io.imread`.
I suggest that this is a good first issue as there is no API design choices here (at least to start) and only needs the
```python
try:
import newway
except ImportError:
import old way
```
dance.
The warnings look like (lifted from a test suite):
```
____________________________________________________________________________ ReaderSequence.test_slice_of_slice ____________________________________________________________________________
pims/tests/test_imseq.py:256: in setUp
self.v = self.klass(self.filename, **self.kwargs)
pims/image_sequence.py:217: in __init__
with self.reader_cls(self._filepaths[0], **self.kwargs) as reader:
pims/image_reader.py:60: in __init__
self._data = Frame(imread(filename, **kwargs), frame_no=0)
../../../../.pybuild/bleeding/lib/python3.11/contextlib.py:155: in __exit__
self.gen.throw(typ, value, traceback)
../../../../.virtualenvs/bleeding/lib/python3.11/site-packages/skimage/io/util.py:43: in file_or_url_context
yield resource_name
../../../../.virtualenvs/bleeding/lib/python3.11/site-packages/skimage/io/_io.py:53: in imread
img = call_plugin('imread', fname, plugin=plugin, **plugin_args)
../../../../.virtualenvs/bleeding/lib/python3.11/site-packages/skimage/io/manage_plugins.py:207: in call_plugin
return func(*args, **kwargs)
../../../../.virtualenvs/bleeding/lib/python3.11/site-packages/skimage/io/_plugins/imageio_plugin.py:10: in imread
return np.asarray(imageio_imread(*args, **kwargs))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
uri = '/home/tcaswell/source/bnl/soft-matter/pims/pims/tests/data/image_sequence3d/file001.png', format = None, kwargs = {}
def imread(uri, format=None, **kwargs):
"""imread(uri, format=None, **kwargs)
Reads an image from the specified file. Returns a numpy array, which
comes with a dict of meta data at its 'meta' attribute.
Note that the image data is returned as-is, and may not always have
a dtype of uint8 (and thus may differ from what e.g. PIL returns).
Parameters
----------
uri : {str, pathlib.Path, bytes, file}
The resource to load the image from, e.g. a filename, pathlib.Path,
http address or file object, see the docs for more info.
format : str
The format to use to read the file. By default imageio selects
the appropriate for you based on the filename and its contents.
kwargs : ...
Further keyword arguments are passed to the reader. See :func:`.help`
to see what arguments are available for a particular format.
"""
> warnings.warn(
"Starting with ImageIO v3 the behavior of this function will switch to that of"
" iio.v3.imread. To keep the current behavior (and make this warning dissapear)"
" use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.",
DeprecationWarning,
)
E DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning dissapear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.
../../../../.virtualenvs/bleeding/lib/python3.11/site-packages/imageio/__init__.py:89: DeprecationWarning
```
| [
{
"content": "__all__ = ['imread', 'imsave']\n\nfrom functools import wraps\nimport numpy as np\nfrom imageio import imread as imageio_imread, imsave\n\n\n@wraps(imageio_imread)\ndef imread(*args, **kwargs):\n return np.asarray(imageio_imread(*args, **kwargs))\n",
"path": "skimage/io/_plugins/imageio_plu... | [
{
"content": "__all__ = ['imread', 'imsave']\n\nfrom functools import wraps\nimport numpy as np\n\ntry:\n # Try using the v2 API directly to avoid a warning from imageio >= 2.16.2\n from imageio.v2 import imread as imageio_imread, imsave\nexcept ImportError:\n from imageio import imread as imageio_imre... | diff --git a/skimage/io/_plugins/imageio_plugin.py b/skimage/io/_plugins/imageio_plugin.py
index c8831e785eb..567d45dd78c 100644
--- a/skimage/io/_plugins/imageio_plugin.py
+++ b/skimage/io/_plugins/imageio_plugin.py
@@ -2,7 +2,12 @@
from functools import wraps
import numpy as np
-from imageio import imread as imageio_imread, imsave
+
+try:
+ # Try using the v2 API directly to avoid a warning from imageio >= 2.16.2
+ from imageio.v2 import imread as imageio_imread, imsave
+except ImportError:
+ from imageio import imread as imageio_imread, imsave
@wraps(imageio_imread)
|
litestar-org__litestar-2681 | Docs: Build errors
### Summary
```
/home/peter/PycharmProjects/litestar/litestar/plugins/base.py:docstring of litestar.plugins.base.InitPluginProtocol.on_app_init:5: ERROR: Error in "code-block" directive:
maximum 1 argument(s) allowed, 14 supplied.
.. code-block:: python
from litestar import Litestar, get
from litestar.di import Provide
from litestar.plugins import InitPluginProtocol
def get_name() -> str:
return "world"
@get("/my-path")
def my_route_handler(name: str) -> dict[str, str]:
return {"hello": name}
class MyPlugin(InitPluginProtocol):
def on_app_init(self, app_config: AppConfig) -> AppConfig:
app_config.dependencies["name"] = Provide(get_name)
app_config.route_handlers.append(my_route_handler)
return app_config
app = Litestar(plugins=[MyPlugin()])
```
And
```
/home/peter/PycharmProjects/litestar/docs/topics/deployment/manually-with-asgi-server.rst:2: WARNING: Title underline too short.
Manually with ASGI server
==========
```
<!-- POLAR PLEDGE BADGE START -->
---
> [!NOTE]
> While we are open for sponsoring on [GitHub Sponsors](https://github.com/sponsors/litestar-org/) and
> [OpenCollective](https://opencollective.com/litestar), we also utilize [Polar.sh](https://polar.sh/) to engage in pledge-based sponsorship.
>
> Check out all issues funded or available for funding [on our Polar.sh Litestar dashboard](https://polar.sh/litestar-org)
> * If you would like to see an issue prioritized, make a pledge towards it!
> * We receive the pledge once the issue is completed & verified
> * This, along with engagement in the community, helps us know which features are a priority to our users.
<a href="https://polar.sh/litestar-org/litestar/issues/2680">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://polar.sh/api/github/litestar-org/litestar/issues/2680/pledge.svg?darkmode=1">
<img alt="Fund with Polar" src="https://polar.sh/api/github/litestar-org/litestar/issues/2680/pledge.svg">
</picture>
</a>
<!-- POLAR PLEDGE BADGE END -->
| [
{
"content": "from __future__ import annotations\n\nfrom typing import TYPE_CHECKING, Any, Iterator, Protocol, TypeVar, Union, cast, runtime_checkable\n\nif TYPE_CHECKING:\n from click import Group\n\n from litestar._openapi.schema_generation import SchemaCreator\n from litestar.config.app import AppCo... | [
{
"content": "from __future__ import annotations\n\nfrom typing import TYPE_CHECKING, Any, Iterator, Protocol, TypeVar, Union, cast, runtime_checkable\n\nif TYPE_CHECKING:\n from click import Group\n\n from litestar._openapi.schema_generation import SchemaCreator\n from litestar.config.app import AppCo... | diff --git a/docs/topics/deployment/manually-with-asgi-server.rst b/docs/topics/deployment/manually-with-asgi-server.rst
index 11f6b89b24..11f00e21a2 100644
--- a/docs/topics/deployment/manually-with-asgi-server.rst
+++ b/docs/topics/deployment/manually-with-asgi-server.rst
@@ -1,5 +1,5 @@
Manually with ASGI server
-==========
+=========================
ASGI (Asynchronous Server Gateway Interface) is intended to provide a standard interface between async Python web frameworks like Litestar, and async web servers. There are several popular ASGI servers available, and you can choose the one that best fits your application's needs.
diff --git a/litestar/plugins/base.py b/litestar/plugins/base.py
index c2214b533f..8b6d536bb2 100644
--- a/litestar/plugins/base.py
+++ b/litestar/plugins/base.py
@@ -33,6 +33,7 @@ def on_app_init(self, app_config: AppConfig) -> AppConfig:
Examples:
.. code-block:: python
+
from litestar import Litestar, get
from litestar.di import Provide
from litestar.plugins import InitPluginProtocol
|
opsdroid__opsdroid-1408 | Duplicated shell prompt
# Description
When I run the hello skill from the shell, I found duplicated shell prompt output. I think there's some issue with the shell connector.
## Steps to Reproduce
```
qidong@ubuntu:~/Documents/opsdroid$ opsdroid start
mybot> hello
Hello qidong
mybot> mybot>
```
## Expected Functionality
There should be only one prompt printed after the skill response.
## Experienced Functionality
One extra prompt is printed.
## Configuration File
```yaml
logging:
console: false
connectors:
websocket:
bot-name: "mybot"
max-connections: 10
connection-timeout: 10
shell:
bot-name: "mybot"
skills:
## Hello (https://github.com/opsdroid/skill-hello)
hello: {}
```
| [
{
"content": "\"\"\"A connector to send messages using the command line.\"\"\"\nimport logging\nimport os\nimport sys\nimport platform\nimport asyncio\n\nfrom opsdroid.connector import Connector, register_event\nfrom opsdroid.events import Message\n\n_LOGGER = logging.getLogger(__name__)\nCONFIG_SCHEMA = {\"bot... | [
{
"content": "\"\"\"A connector to send messages using the command line.\"\"\"\nimport logging\nimport os\nimport sys\nimport platform\nimport asyncio\n\nfrom opsdroid.connector import Connector, register_event\nfrom opsdroid.events import Message\n\n_LOGGER = logging.getLogger(__name__)\nCONFIG_SCHEMA = {\"bot... | diff --git a/opsdroid/connector/shell/__init__.py b/opsdroid/connector/shell/__init__.py
index be6ea62e9..4adfea5a2 100644
--- a/opsdroid/connector/shell/__init__.py
+++ b/opsdroid/connector/shell/__init__.py
@@ -125,7 +125,6 @@ async def respond(self, message):
_LOGGER.debug(_("Responding with: %s."), message.text)
self.clear_prompt()
print(message.text)
- self.draw_prompt()
async def disconnect(self):
"""Disconnects the connector."""
diff --git a/tests/test_connector_shell.py b/tests/test_connector_shell.py
index 70e000aeb..b1a040a6d 100644
--- a/tests/test_connector_shell.py
+++ b/tests/test_connector_shell.py
@@ -161,7 +161,7 @@ async def test_respond(self):
with contextlib.redirect_stdout(f):
await self.connector.respond(message)
prompt = f.getvalue()
- self.assertEqual(prompt.strip(), "Hi\nopsdroid>")
+ self.assertEqual(prompt.strip(), "Hi")
async def test_disconnect(self):
connector = ConnectorShell({}, opsdroid=OpsDroid())
|
opsdroid__opsdroid-946 | PyPI deployments are failing
Looks like PyPI deployments are failing. `v0.15.1` and `v0.15.2` haven't gone out.
```
HTTPError: 400 Client Error: The description failed to render in the default format of reStructuredText. See https://pypi.org/help/#description-content-type for more information. for url: https://upload.pypi.org/legacy/
```
PyPI deployments are failing
Looks like PyPI deployments are failing. `v0.15.1` and `v0.15.2` haven't gone out.
```
HTTPError: 400 Client Error: The description failed to render in the default format of reStructuredText. See https://pypi.org/help/#description-content-type for more information. for url: https://upload.pypi.org/legacy/
```
| [
{
"content": "#!/usr/bin/env python3\nimport os\nfrom setuptools import setup, find_packages\nfrom setuptools.command.build_py import build_py\nfrom setuptools.command.sdist import sdist\nfrom setuptools.command.develop import develop\nimport versioneer\n\nPACKAGE_NAME = 'opsdroid'\nHERE = os.path.abspath(os.pa... | [
{
"content": "#!/usr/bin/env python3\nimport os\nfrom setuptools import setup, find_packages\nfrom setuptools.command.build_py import build_py\nfrom setuptools.command.sdist import sdist\nfrom setuptools.command.develop import develop\nimport versioneer\n\nPACKAGE_NAME = 'opsdroid'\nHERE = os.path.abspath(os.pa... | diff --git a/setup.py b/setup.py
index 588670838..f9ed22303 100644
--- a/setup.py
+++ b/setup.py
@@ -55,6 +55,7 @@ def run(self):
author_email='jacob@tom.linson.uk',
description='An open source ChatOps bot framework.',
long_description=README,
+ long_description_content_type='text/markdown',
packages=PACKAGES,
include_package_data=True,
zip_safe=False,
|
netbox-community__netbox-14935 | Typo in DataSourceBulkEditForm
### Deployment Type
Self-hosted
### NetBox Version
v3.7.1
### Python Version
3.8
### Steps to Reproduce
"lavel" is defined as "Enforce unique space", but I think the correct definition is "Enabled".
https://github.com/netbox-community/netbox/blob/487f1ccfde26ef3c1f8a28089826acc0cd6fadb2/netbox/core/forms/bulk_edit.py#L21-L25
- Add a new data source
- Editing 1 Data Sources
### Expected Behavior
Enabled
### Observed Behavior
Enforce unique space
| [
{
"content": "from django import forms\nfrom django.utils.translation import gettext_lazy as _\n\nfrom core.models import *\nfrom netbox.forms import NetBoxModelBulkEditForm\nfrom netbox.utils import get_data_backend_choices\nfrom utilities.forms.fields import CommentField\nfrom utilities.forms.widgets import B... | [
{
"content": "from django import forms\nfrom django.utils.translation import gettext_lazy as _\n\nfrom core.models import *\nfrom netbox.forms import NetBoxModelBulkEditForm\nfrom netbox.utils import get_data_backend_choices\nfrom utilities.forms.fields import CommentField\nfrom utilities.forms.widgets import B... | diff --git a/netbox/core/forms/bulk_edit.py b/netbox/core/forms/bulk_edit.py
index dcc92c6f07c..bc2ef8fc92e 100644
--- a/netbox/core/forms/bulk_edit.py
+++ b/netbox/core/forms/bulk_edit.py
@@ -21,7 +21,7 @@ class DataSourceBulkEditForm(NetBoxModelBulkEditForm):
enabled = forms.NullBooleanField(
required=False,
widget=BulkEditNullBooleanSelect(),
- label=_('Enforce unique space')
+ label=_('Enabled')
)
description = forms.CharField(
label=_('Description'),
|
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