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40,113 | 167,770 | 101 | pandas/core/groupby/groupby.py | 18 | 13 | def rolling(self, *args, **kwargs) -> RollingGroupby:
from pandas.core.window import RollingGroupby
| TYP: more return annotations in core/ (#47618)
* TYP: more return annotations in core/
* from __future__ import annotations
* more __future__ | rolling | f65417656ba8c59438d832b6e2a431f78d40c21c | pandas | groupby.py | 9 | 12 | https://github.com/pandas-dev/pandas.git | 1 | 48 | 0 | 17 | 71 | Python | {
"docstring": "\n Return a rolling grouper, providing rolling functionality per group.\n ",
"language": "en",
"n_whitespaces": 24,
"n_words": 9,
"vocab_size": 8
} | def rolling(self, *args, **kwargs) -> RollingGroupby:
from pandas.core.window import RollingGroupby
return RollingGroupby(
self._selected_obj,
*args,
_grouper=self.grouper,
_as_index=self.as_index,
**kwargs,
)
|
|
42,064 | 176,730 | 417 | networkx/generators/degree_seq.py | 179 | 35 | def expected_degree_graph(w, seed=None, selfloops=True):
r
n = len(w)
G = nx.empty_graph(n)
# If there are no nodes are no edges in the graph, return the empty graph.
if n == 0 or max(w) == 0:
return G
rho = 1 / sum(w)
# Sort the weights in decreasing order. The original order of the
# weights dictates the order of the (integer) node labels, so we
# need to remember the permutation applied in the sorting.
order = sorted(enumerate(w), key=itemgetter(1), reverse=True)
mapping = {c: u for c, (u, v) in enumerate(order)}
seq = [v for u, v in order]
last = n
if not selfloops:
last -= 1
for u in range(last):
v = u
if not selfloops:
v += 1
factor = seq[u] * rho
p = min(seq[v] * factor, 1)
while v < n and p > 0:
if p != 1:
r = seed.random()
v += math.floor(math.log(r, 1 - p))
if v < n:
q = min(seq[v] * factor, 1)
if seed.random() < q / p:
G.add_edge(mapping[u | Remove redundant py2 numeric conversions (#5661)
* Remove redundant float conversion
* Remove redundant int conversion
* Use integer division
Co-authored-by: Miroslav Šedivý <6774676+eumiro@users.noreply.github.com> | expected_degree_graph | 2a05ccdb07cff88e56661dee8a9271859354027f | networkx | degree_seq.py | 17 | 100 | https://github.com/networkx/networkx.git | 13 | 240 | 0 | 97 | 375 | Python | {
"docstring": "Returns a random graph with given expected degrees.\n\n Given a sequence of expected degrees $W=(w_0,w_1,\\ldots,w_{n-1})$\n of length $n$ this algorithm assigns an edge between node $u$ and\n node $v$ with probability\n\n .. math::\n\n p_{uv} = \\frac{w_u w_v}{\\sum_k w_k} .\n\n Parameters\n ----------\n w : list\n The list of expected degrees.\n selfloops: bool (default=True)\n Set to False to remove the possibility of self-loop edges.\n seed : integer, random_state, or None (default)\n Indicator of random number generation state.\n See :ref:`Randomness<randomness>`.\n\n Returns\n -------\n Graph\n\n Examples\n --------\n >>> z = [10 for i in range(100)]\n >>> G = nx.expected_degree_graph(z)\n\n Notes\n -----\n The nodes have integer labels corresponding to index of expected degrees\n input sequence.\n\n The complexity of this algorithm is $\\mathcal{O}(n+m)$ where $n$ is the\n number of nodes and $m$ is the expected number of edges.\n\n The model in [1]_ includes the possibility of self-loop edges.\n Set selfloops=False to produce a graph without self loops.\n\n For finite graphs this model doesn't produce exactly the given\n expected degree sequence. Instead the expected degrees are as\n follows.\n\n For the case without self loops (selfloops=False),\n\n .. math::\n\n E[deg(u)] = \\sum_{v \\ne u} p_{uv}\n = w_u \\left( 1 - \\frac{w_u}{\\sum_k w_k} \\right) .\n\n\n NetworkX uses the standard convention that a self-loop edge counts 2\n in the degree of a node, so with self loops (selfloops=True),\n\n .. math::\n\n E[deg(u)] = \\sum_{v \\ne u} p_{uv} + 2 p_{uu}\n = w_u \\left( 1 + \\frac{w_u}{\\sum_k w_k} \\right) .\n\n References\n ----------\n .. [1] Fan Chung and L. Lu, Connected components in random graphs with\n given expected degree sequences, Ann. Combinatorics, 6,\n pp. 125-145, 2002.\n .. [2] Joel Miller and Aric Hagberg,\n Efficient generation of networks with given expected degrees,\n in Algorithms and Models for the Web-Graph (WAW 2011),\n Alan Frieze, Paul Horn, and Paweł Prałat (Eds), LNCS 6732,\n pp. 115-126, 2011.\n ",
"language": "en",
"n_whitespaces": 524,
"n_words": 298,
"vocab_size": 173
} | def expected_degree_graph(w, seed=None, selfloops=True):
r
n = len(w)
G = nx.empty_graph(n)
# If there are no nodes are no edges in the graph, return the empty graph.
if n == 0 or max(w) == 0:
return G
rho = 1 / sum(w)
# Sort the weights in decreasing order. The original order of the
# weights dictates the order of the (integer) node labels, so we
# need to remember the permutation applied in the sorting.
order = sorted(enumerate(w), key=itemgetter(1), reverse=True)
mapping = {c: u for c, (u, v) in enumerate(order)}
seq = [v for u, v in order]
last = n
if not selfloops:
last -= 1
for u in range(last):
v = u
if not selfloops:
v += 1
factor = seq[u] * rho
p = min(seq[v] * factor, 1)
while v < n and p > 0:
if p != 1:
r = seed.random()
v += math.floor(math.log(r, 1 - p))
if v < n:
q = min(seq[v] * factor, 1)
if seed.random() < q / p:
G.add_edge(mapping[u], mapping[v])
v += 1
p = q
return G
|
|
2,897 | 19,151 | 208 | mlflow/models/evaluation/base.py | 49 | 22 | def save(self, path):
os.makedirs(path, | Improve evaluation api (#5256)
* init
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
* update
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
* update
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
* update doc
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
* update doc
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
* address comments
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
* update doc
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
* add shap limitation on value type
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
* fix format
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
* update
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
* update
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
* update
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
* update
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
* update
Signed-off-by: Weichen Xu <weichen.xu@databricks.com> | save | 4c58179509e6f6047789efb0a95c2b0e20cb6c8f | mlflow | base.py | 13 | 17 | https://github.com/mlflow/mlflow.git | 3 | 153 | 0 | 36 | 253 | Python | {
"docstring": "Write the evaluation results to the specified local filesystem path",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 9
} | def save(self, path):
os.makedirs(path, exist_ok=True)
with open(os.path.join(path, "metrics.json"), "w") as fp:
json.dump(self.metrics, fp)
artifacts_metadata = {
artifact_name: {
"uri": artifact.uri,
"class_name": _get_fully_qualified_class_name(artifact),
}
for artifact_name, artifact in self.artifacts.items()
}
with open(os.path.join(path, "artifacts_metadata.json"), "w") as fp:
json.dump(artifacts_metadata, fp)
artifacts_dir = os.path.join(path, "artifacts")
os.mkdir(artifacts_dir)
for artifact_name, artifact in self.artifacts.items():
artifact._save(os.path.join(artifacts_dir, artifact_name))
|
|
18,592 | 89,933 | 154 | tests/sentry/integrations/slack/test_message_builder.py | 51 | 25 | def test_build_group_generic_issue_attachment(self):
event = self.store_event(
data={"message": "Hello world", "level": "error"}, project_id=self.project.id
)
event = event.for_group(event.groups[0])
occurrence = self.build_occurrence(level="info")
occurrence.save(project_id=self.project.id)
event.occurrence = occurrence
event.group.type = GroupType.PROFILE_BLOCKED_THREAD
attachments = SlackIssuesMessageBuilder(group=event.group, event=event).build()
assert attachments["title"] == occurrence.issue_title
assert attachments["text"] == occurrence.evidence_display[0].value
assert attachments["fallback"] == f"[{self.project.slug}] {occurrence.issue_title}"
assert attachments["color"] = | feat(integrations): Support generic issue type alerts (#42110)
Add support for issue alerting integrations that use the message builder
(Slack and MSTeams) for generic issue types.
Preview text for Slack alert:
<img width="350" alt="Screen Shot 2022-12-08 at 4 07 16 PM"
src="https://user-images.githubusercontent.com/29959063/206593405-7a206d88-a31a-4e85-8c15-1f7534733ca7.png">
Slack generic issue alert shows the `occurrence.issue_title` and the
"important" evidence value
<img width="395" alt="Screen Shot 2022-12-08 at 4 11 20 PM"
src="https://user-images.githubusercontent.com/29959063/206593408-6942d74d-4238-4df9-bfee-601ce2bc1098.png">
MSTeams generic issue alert shows the `occurrence.issue_title` and the
"important" evidence value
<img width="654" alt="Screen Shot 2022-12-08 at 4 13 45 PM"
src="https://user-images.githubusercontent.com/29959063/206593410-2773746a-16b3-4652-ba2c-a7d5fdc76992.png">
Fixes #42047 | test_build_group_generic_issue_attachment | 3255fa4ebb9fbc1df6bb063c0eb77a0298ca8f72 | sentry | test_message_builder.py | 12 | 14 | https://github.com/getsentry/sentry.git | 1 | 137 | 0 | 38 | 249 | Python | {
"docstring": "Test that a generic issue type's Slack alert contains the expected values",
"language": "en",
"n_whitespaces": 11,
"n_words": 12,
"vocab_size": 12
} | def test_build_group_generic_issue_attachment(self):
event = self.store_event(
data={"message": "Hello world", "level": "error"}, project_id=self.project.id
)
event = event.for_group(event.groups[0])
occurrence = self.build_occurrence(level="info")
occurrence.save(project_id=self.project.id)
event.occurrence = occurrence
event.group.type = GroupType.PROFILE_BLOCKED_THREAD
attachments = SlackIssuesMessageBuilder(group=event.group, event=event).build()
assert attachments["title"] == occurrence.issue_title
assert attachments["text"] == occurrence.evidence_display[0].value
assert attachments["fallback"] == f"[{self.project.slug}] {occurrence.issue_title}"
assert attachments["color"] == "#2788CE" # blue for info level
|
|
42,906 | 179,114 | 127 | xlib/image/ImageProcessor.py | 45 | 14 | def apply(self, func, mask=None) -> 'ImageProcessor':
img = orig_img = self._img
img = func(img).astype(orig_img.dtype)
if img.ndim != 4:
raise Exception('func used in ImageProcessor.apply changed format of image')
if mask is not None:
| ImageProcessor.py refactoring | apply | b3bc4e734528d3b186c3a38a6e73e106c3555cc7 | DeepFaceLive | ImageProcessor.py | 13 | 21 | https://github.com/iperov/DeepFaceLive.git | 3 | 82 | 0 | 34 | 137 | Python | {
"docstring": "\n apply your own function on internal image\n\n image has NHWC format. Do not change format, but dims can be changed.\n\n func callable (img) -> img\n\n example:\n\n .apply( lambda img: img-[102,127,63] )\n ",
"language": "en",
"n_whitespaces": 79,
"n_words": 31,
"vocab_size": 30
} | def apply(self, func, mask=None) -> 'ImageProcessor':
img = orig_img = self._img
img = func(img).astype(orig_img.dtype)
if img.ndim != 4:
raise Exception('func used in ImageProcessor.apply changed format of image')
if mask is not None:
mask = self._check_normalize_mask(mask)
img = ne.evaluate('orig_img*(1-mask) + img*mask').astype(orig_img.dtype)
self._img = img
return self
|
|
7,073 | 39,007 | 110 | recommenders/models/rbm/rbm.py | 38 | 17 | def predict(self, x):
# start the timer
self.timer.start()
v_, _ = self | removed time from returning args | predict | 843dba903757d592f7703a83ebd75eb3ffb46f6f | recommenders | rbm.py | 12 | 7 | https://github.com/microsoft/recommenders.git | 1 | 65 | 0 | 30 | 111 | Python | {
"docstring": "Returns the inferred ratings. This method is similar to recommend_k_items() with the\n exceptions that it returns all the inferred ratings\n\n Basic mechanics:\n\n The method samples new ratings from the learned joint distribution, together with\n their probabilities. The input x must have the same number of columns as the one used\n for training the model, i.e. the same number of items, but it can have an arbitrary number\n of rows (users).\n\n Args:\n x (numpy.ndarray, int32): Input user/affinity matrix. Note that this can be a single vector, i.e.\n the ratings of a single user.\n\n Returns:\n numpy.ndarray, float:\n - A matrix with the inferred ratings.\n - The elapsed time for predediction.\n ",
"language": "en",
"n_whitespaces": 226,
"n_words": 108,
"vocab_size": 73
} | def predict(self, x):
# start the timer
self.timer.start()
v_, _ = self.eval_out() # evaluate the ratings and the associated probabilities
vp = self.sess.run(v_, feed_dict={self.vu: x})
# stop the timer
self.timer.stop()
log.info("Done inference, time %f2" % self.timer.interval)
return vp
|
|
55,394 | 218,569 | 74 | python3.10.4/Lib/json/decoder.py | 24 | 11 | def raw_decode(self, s, idx=0):
try:
obj, end = self.scan_once(s, idx)
except StopIteration as err:
raise JSONDecodeError("Expecting value", s, err.val | add python 3.10.4 for windows | raw_decode | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | decoder.py | 11 | 6 | https://github.com/XX-net/XX-Net.git | 2 | 48 | 0 | 21 | 76 | Python | {
"docstring": "Decode a JSON document from ``s`` (a ``str`` beginning with\n a JSON document) and return a 2-tuple of the Python\n representation and the index in ``s`` where the document ended.\n\n This can be used to decode a JSON document from a string that may\n have extraneous data at the end.\n\n ",
"language": "en",
"n_whitespaces": 85,
"n_words": 50,
"vocab_size": 36
} | def raw_decode(self, s, idx=0):
try:
obj, end = self.scan_once(s, idx)
except StopIteration as err:
raise JSONDecodeError("Expecting value", s, err.value) from None
return obj, end
|
|
@not_implemented_for("multigraph")
@not_implemented_for("directed") | 41,965 | 176,561 | 45 | networkx/algorithms/bridges.py | 14 | 7 | def has_bridges(G, root=None):
try:
next(bridges | Improve bridges documentation (#5519)
* Fix bridges documentation
* Revert source code modification
* Revert raise errors for multigraphs | has_bridges | aa1f40a93a882db304e9a06c2a11d93b2532d80a | networkx | bridges.py | 11 | 7 | https://github.com/networkx/networkx.git | 2 | 28 | 1 | 13 | 70 | Python | {
"docstring": "Decide whether a graph has any bridges.\n\n A *bridge* in a graph is an edge whose removal causes the number of\n connected components of the graph to increase.\n\n Parameters\n ----------\n G : undirected graph\n\n root : node (optional)\n A node in the graph `G`. If specified, only the bridges in the\n connected component containing this node will be considered.\n\n Returns\n -------\n bool\n Whether the graph (or the connected component containing `root`)\n has any bridges.\n\n Raises\n ------\n NodeNotFound\n If `root` is not in the graph `G`.\n\n NetworkXNotImplemented\n If `G` is a directed graph.\n\n Examples\n --------\n The barbell graph with parameter zero has a single bridge::\n\n >>> G = nx.barbell_graph(10, 0)\n >>> nx.has_bridges(G)\n True\n\n On the other hand, the cycle graph has no bridges::\n\n >>> G = nx.cycle_graph(5)\n >>> nx.has_bridges(G)\n False\n\n Notes\n -----\n This implementation uses the :func:`networkx.bridges` function, so\n it shares its worst-case time complexity, $O(m + n)$, ignoring\n polylogarithmic factors, where $n$ is the number of nodes in the\n graph and $m$ is the number of edges.\n\n ",
"language": "en",
"n_whitespaces": 318,
"n_words": 167,
"vocab_size": 106
} | def has_bridges(G, root=None):
try:
next(bridges(G))
except StopIteration:
return False
else:
return True
@not_implemented_for("multigraph")
@not_implemented_for("directed") |
12,520 | 61,338 | 112 | .venv/lib/python3.8/site-packages/pip/_internal/utils/wheel.py | 65 | 13 | def wheel_metadata(source, dist_info_dir):
# type: (ZipFile, str) -> Message
path = f"{dist_info_dir}/WHEEL"
# Zip file path separators must be /
wheel_contents = read_wheel_metadata_file(source, path)
try:
wheel_text = wheel_contents.decode()
except UnicodeDecodeError as e:
raise UnsupportedWheel(f"error decoding {path!r}: {e!r}")
# FeedParser (used by Parser) does not raise any exceptions. The returned
# message may have .defects populated, but for backwards-compatibility | upd; format | wheel_metadata | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | transferlearning | wheel.py | 12 | 8 | https://github.com/jindongwang/transferlearning.git | 2 | 49 | 0 | 57 | 103 | Python | {
"docstring": "Return the WHEEL metadata of an extracted wheel, if possible.\n Otherwise, raise UnsupportedWheel.\n ",
"language": "en",
"n_whitespaces": 19,
"n_words": 13,
"vocab_size": 13
} | def wheel_metadata(source, dist_info_dir):
# type: (ZipFile, str) -> Message
path = f"{dist_info_dir}/WHEEL"
# Zip file path separators must be /
wheel_contents = read_wheel_metadata_file(source, path)
try:
wheel_text = wheel_contents.decode()
except UnicodeDecodeError as e:
raise UnsupportedWheel(f"error decoding {path!r}: {e!r}")
# FeedParser (used by Parser) does not raise any exceptions. The returned
# message may have .defects populated, but for backwards-compatibility we
# currently ignore them.
return Parser().parsestr(wheel_text)
|
|
21,852 | 104,416 | 172 | src/datasets/table.py | 40 | 14 | def remove_column(self, i, *args, **kwargs):
table = self.table.remove_column(i, *args, **kwargs)
name = self.table.column_names[i]
blocks = []
for tables in self.blocks:
blocks.append(
[
t.remove_colu | Update docs to new frontend/UI (#3690)
* WIP: update docs to new UI
* make style
* Rm unused
* inject_arrow_table_documentation __annotations__
* hasattr(arrow_table_method, "__annotations__")
* Update task_template.rst
* Codeblock PT-TF-SPLIT
* Convert loading scripts
* Convert docs to mdx
* Fix mdx
* Add <Tip>
* Convert mdx tables
* Fix codeblock
* Rm unneded hashlinks
* Update index.mdx
* Redo dev change
* Rm circle ci `build_doc` & `deploy_doc`
* Rm unneeded files
* Update docs reamde
* Standardize to `Example::`
* mdx logging levels doc
* Table properties inject_arrow_table_documentation
* ``` to ```py mdx
* Add Tips mdx
* important,None -> <Tip warning={true}>
* More misc
* Center imgs
* Update instllation page
* `setup.py` docs section
* Rm imgs since they are in hf.co
* Update docs/source/access.mdx
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update index mdx
* Update docs/source/access.mdx
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* just `Dataset` obj
* Addedversion just italics
* Update ReadInstruction doc example syntax
* Change docstring for `prepare_for_task`
* Chore
* Remove `code` syntax from headings
* Rm `code` syntax from headings
* Hashlink backward compatability
* S3FileSystem doc
* S3FileSystem doc updates
* index.mdx updates
* Add darkmode gifs
* Index logo img css classes
* Index mdx dataset logo img size
* Docs for DownloadMode class
* Doc DownloadMode table
* format docstrings
* style
* Add doc builder scripts (#3790)
* add doc builder scripts
* fix docker image
* Docs new UI actions no self hosted (#3793)
* No self hosted
* replace doc injection by actual docstrings
* Docstring formatted
Co-authored-by: Quentin Lhoest <lhoest.q@gmail.com>
Co-authored-by: Mishig Davaadorj <dmishig@gmail.com>
Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Mishig Davaadorj <dmishig@gmail.com>
* Rm notebooks from docs actions since they dont exi
* Update tsting branch
* More docstring
* Chore
* bump up node version
* bump up node
* ``` -> ```py for audio_process.mdx
* Update .github/workflows/build_documentation.yml
Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>
* Uodate dev doc build
* remove run on PR
* fix action
* Fix gh doc workflow
* forgot this change when merging master
* Update build doc
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Quentin Lhoest <lhoest.q@gmail.com>
Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr> | remove_column | e35be138148333078284b942ccc9ed7b1d826f97 | datasets | table.py | 16 | 12 | https://github.com/huggingface/datasets.git | 4 | 96 | 0 | 29 | 145 | Python | {
"docstring": "\n Create new Table with the indicated column removed.\n\n Args:\n i (:obj:`int`):\n Index of column to remove.\n\n Returns:\n :class:`datasets.table.Table`:\n New table without the column.\n ",
"language": "en",
"n_whitespaces": 104,
"n_words": 23,
"vocab_size": 21
} | def remove_column(self, i, *args, **kwargs):
table = self.table.remove_column(i, *args, **kwargs)
name = self.table.column_names[i]
blocks = []
for tables in self.blocks:
blocks.append(
[
t.remove_column(t.column_names.index(name), *args, **kwargs) if name in t.column_names else t
for t in tables
]
)
return ConcatenationTable(table, blocks)
|
|
77,897 | 264,886 | 53 | netbox/dcim/tests/test_models.py | 14 | 18 | def test_cable_cannot_terminate_to_a_wireless_interface(self):
wireless_interface = Interface(device=self.device1, name="W1", type=InterfaceTypeChoices.TYPE_80211A)
cable = Cable(a_terminations=[self.interface2], b_terminations=[wireless_interface])
with self.assertRaises(ValidationError):
cable.clean()
| Update Cable instantiations to match new signature | test_cable_cannot_terminate_to_a_wireless_interface | 3a461d02793e6f9d41c2b1a92647e691de1abaac | netbox | test_models.py | 11 | 5 | https://github.com/netbox-community/netbox.git | 1 | 57 | 0 | 13 | 95 | Python | {
"docstring": "\n A cable cannot terminate to a wireless interface\n ",
"language": "en",
"n_whitespaces": 23,
"n_words": 8,
"vocab_size": 8
} | def test_cable_cannot_terminate_to_a_wireless_interface(self):
wireless_interface = Interface(device=self.device1, name="W1", type=InterfaceTypeChoices.TYPE_80211A)
cable = Cable(a_terminations=[self.interface2], b_terminations=[wireless_interface])
with self.assertRaises(ValidationError):
cable.clean()
|
|
50,917 | 204,838 | 114 | django/db/backends/base/creation.py | 43 | 7 | def get_test_db_clone_settings(self, suffix):
# When this function is called, the test database has been created
# already and its name has been copied to | Refs #33476 -- Reformatted code with Black. | get_test_db_clone_settings | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | django | creation.py | 11 | 6 | https://github.com/django/django.git | 1 | 35 | 0 | 38 | 63 | Python | {
"docstring": "\n Return a modified connection settings dict for the n-th clone of a DB.\n ",
"language": "en",
"n_whitespaces": 28,
"n_words": 13,
"vocab_size": 12
} | def get_test_db_clone_settings(self, suffix):
# When this function is called, the test database has been created
# already and its name has been copied to settings_dict['NAME'] so
# we don't need to call _get_test_db_name.
orig_settings_dict = self.connection.settings_dict
return {
**orig_settings_dict,
"NAME": "{}_{}".format(orig_settings_dict["NAME"], suffix),
}
|
|
55,005 | 217,907 | 52 | python3.10.4/Lib/imaplib.py | 17 | 10 | def open(self, host='', port=IMAP4_PORT, timeout=None):
self.host = host
self.port = port
self.sock = self._create_socket(timeout)
self.file = self.sock.makefile('rb')
| add python 3.10.4 for windows | open | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | imaplib.py | 9 | 5 | https://github.com/XX-net/XX-Net.git | 1 | 50 | 0 | 14 | 83 | Python | {
"docstring": "Setup connection to remote server on \"host:port\"\n (default: localhost:standard IMAP4 port).\n This connection will be used by the routines:\n read, readline, send, shutdown.\n ",
"language": "en",
"n_whitespaces": 59,
"n_words": 23,
"vocab_size": 22
} | def open(self, host='', port=IMAP4_PORT, timeout=None):
self.host = host
self.port = port
self.sock = self._create_socket(timeout)
self.file = self.sock.makefile('rb')
|
|
44,257 | 183,574 | 42 | src/textual/_terminal_features.py | 10 | 7 | def synchronized_output_end_sequence(self) -> str:
if self.synchronised_output:
return | [terminal buffering] Address PR feedback | synchronized_output_end_sequence | 7f27e70440c177b2a047b7f74a78ed5cd5b4b596 | textual | _terminal_features.py | 10 | 13 | https://github.com/Textualize/textual.git | 2 | 25 | 0 | 9 | 45 | Python | {
"docstring": "\n Returns the ANSI sequence that we should send to the terminal to tell it that\n it should stop buffering the content we're about to send.\n If the terminal doesn't seem to support synchronised updates the string will be empty.\n\n Returns:\n str: the \"synchronised output stop\" ANSI sequence. It will be ab empty string\n if the terminal emulator doesn't seem to support the \"synchronised updates\" mode.\n ",
"language": "en",
"n_whitespaces": 127,
"n_words": 65,
"vocab_size": 41
} | def synchronized_output_end_sequence(self) -> str:
if self.synchronised_output:
return TERMINAL_MODES_ANSI_SEQUENCES[Mode.SynchronizedOutput]["end_sync"]
return ""
|
|
39,253 | 162,681 | 98 | frequency_response.py | 42 | 23 | def _band_penalty_coefficients(self, fc, q, gain, filter_frs):
ref_frs = biquad.digital_coeffs(self.frequenc | Improved quality regularization to a point where it works well. 10 kHz to 20 kHz is RMSE is calculated from the average levels. Split neo PEQ notebook by band and Q. | _band_penalty_coefficients | f6021faf2a8e62f88a8d6979ce812dcb71133a8f | AutoEq | frequency_response.py | 12 | 8 | https://github.com/jaakkopasanen/AutoEq.git | 1 | 121 | 0 | 34 | 176 | Python | {
"docstring": "Calculates penalty coefficients for filters if their transition bands extend beyond Nyquist frequency\n\n The calculation is based on ratio of frequency response integrals between 44.1 kHz and 192 kHz\n\n Args:\n fc: Filter center frequencies, 1-D array\n q: Filter qualities, 1-D array\n gain: Filter gains, 1-D array\n filter_frs: Filter frequency responses, 2-D array, one fr per row\n\n Returns:\n Column array of penalty coefficients, one per filter\n ",
"language": "en",
"n_whitespaces": 148,
"n_words": 65,
"vocab_size": 50
} | def _band_penalty_coefficients(self, fc, q, gain, filter_frs):
ref_frs = biquad.digital_coeffs(self.frequency, 192e3, *biquad.peaking(fc, q, gain, fs=192e3))
est_sums = np.sum(filter_frs, axis=1)
ref_sums = np.sum(ref_frs, axis=1)
penalties = np.zeros((len(fc),))
mask = np.squeeze(ref_sums) != 0.0
penalties[mask] = est_sums[mask] / ref_sums[mask]
return 10 * (1 - np.expand_dims(penalties, 1))
|
|
76,664 | 261,153 | 201 | sklearn/ensemble/tests/test_voting.py | 104 | 22 | def test_predict_on_toy_problem(global_random_seed):
clf1 = LogisticRegression(random_state=global_random_seed)
clf2 = RandomForestClassifier(n_estimators=10, random_state=global_random_seed)
clf3 = GaussianNB()
X = np.array(
[[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2], [2.1, 1.4], [3.1, 2.3]]
)
y = np.array([1, 1, 1, 2, 2, 2])
assert_array_equal(clf1.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
assert_array_equal(clf2.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
assert_array_equal(clf3.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
eclf = VotingClassifier(
estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)],
voting="hard",
weights=[1, 1, 1],
)
assert_array_equal(eclf.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
eclf = VotingClassifier(
estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)],
voting="soft",
weights=[1, 1, 1],
)
assert_array | TST use global_random_seed in sklearn/ensemble/tests/test_voting.py (#24282)
Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> | test_predict_on_toy_problem | 02b04cb3ecfc5fce1f627281c312753f3b4b8494 | scikit-learn | test_voting.py | 12 | 23 | https://github.com/scikit-learn/scikit-learn.git | 1 | 357 | 0 | 48 | 469 | Python | {
"docstring": "Manually check predicted class labels for toy dataset.",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 8
} | def test_predict_on_toy_problem(global_random_seed):
clf1 = LogisticRegression(random_state=global_random_seed)
clf2 = RandomForestClassifier(n_estimators=10, random_state=global_random_seed)
clf3 = GaussianNB()
X = np.array(
[[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2], [2.1, 1.4], [3.1, 2.3]]
)
y = np.array([1, 1, 1, 2, 2, 2])
assert_array_equal(clf1.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
assert_array_equal(clf2.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
assert_array_equal(clf3.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
eclf = VotingClassifier(
estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)],
voting="hard",
weights=[1, 1, 1],
)
assert_array_equal(eclf.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
eclf = VotingClassifier(
estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)],
voting="soft",
weights=[1, 1, 1],
)
assert_array_equal(eclf.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
|
|
76,257 | 260,448 | 29 | sklearn/feature_extraction/_dict_vectorizer.py | 8 | 7 | def fit_transform(self, X, y=None):
self._validate_params()
return self._tran | MAINT Param validation for Dictvectorizer (#23820) | fit_transform | 5a850eb044ca07f1f3bcb1b284116d6f2d37df1b | scikit-learn | _dict_vectorizer.py | 8 | 3 | https://github.com/scikit-learn/scikit-learn.git | 1 | 28 | 0 | 8 | 45 | Python | {
"docstring": "Learn a list of feature name -> indices mappings and transform X.\n\n Like fit(X) followed by transform(X), but does not require\n materializing X in memory.\n\n Parameters\n ----------\n X : Mapping or iterable over Mappings\n Dict(s) or Mapping(s) from feature names (arbitrary Python\n objects) to feature values (strings or convertible to dtype).\n\n .. versionchanged:: 0.24\n Accepts multiple string values for one categorical feature.\n\n y : (ignored)\n Ignored parameter.\n\n Returns\n -------\n Xa : {array, sparse matrix}\n Feature vectors; always 2-d.\n ",
"language": "en",
"n_whitespaces": 217,
"n_words": 78,
"vocab_size": 69
} | def fit_transform(self, X, y=None):
self._validate_params()
return self._transform(X, fitting=True)
|
|
117,565 | 321,150 | 761 | qutebrowser/browser/webengine/webenginetab.py | 125 | 54 | def _on_feature_permission_requested(self, url, feature):
page = self._widget.page()
grant_permission = functools.partial(
page.setFeaturePermission, url, feature,
QWebEnginePage.PermissionPolicy.PermissionGrantedByUser)
deny_permission = functools.partial(
page.setFeaturePermission, url, feature,
QWebEnginePage.PermissionPolicy.PermissionDeniedByUser)
permission_str = debug.qenum_key(QWebEnginePage, feature)
if not url.isValid():
# WORKAROUND for https://bugreports.qt.io/browse/QTBUG-85116
is_qtbug = (qtutils.version_check('5.15.0',
compiled=False,
exact=True) and
self._tab.is_private and
feature == QWebEnginePage.Feature.Notifications)
logger = log.webview.debug if is_qtbug else log.webview.warning
logger("Ignoring feature permission {} for invalid URL {}".format(
permission_str, url))
deny_permission()
return
if feature not in self._options:
log.webview.error("Unhandled feature permission {}".format(
permission_str))
deny_permission()
return
if (
feature in [QWebEnginePage.Feature.DesktopVideoCapture,
QWebEnginePage.Feature.DesktopAudioVideoCapture] and
qtutils.version_check('5.13', compiled= | Run scripts/dev/rewrite_enums.py | _on_feature_permission_requested | 0877fb0d78635692e481c8bde224fac5ad0dd430 | qutebrowser | webenginetab.py | 14 | 44 | https://github.com/qutebrowser/qutebrowser.git | 10 | 301 | 0 | 84 | 470 | Python | {
"docstring": "Ask the user for approval for geolocation/media/etc..",
"language": "en",
"n_whitespaces": 6,
"n_words": 7,
"vocab_size": 6
} | def _on_feature_permission_requested(self, url, feature):
page = self._widget.page()
grant_permission = functools.partial(
page.setFeaturePermission, url, feature,
QWebEnginePage.PermissionPolicy.PermissionGrantedByUser)
deny_permission = functools.partial(
page.setFeaturePermission, url, feature,
QWebEnginePage.PermissionPolicy.PermissionDeniedByUser)
permission_str = debug.qenum_key(QWebEnginePage, feature)
if not url.isValid():
# WORKAROUND for https://bugreports.qt.io/browse/QTBUG-85116
is_qtbug = (qtutils.version_check('5.15.0',
compiled=False,
exact=True) and
self._tab.is_private and
feature == QWebEnginePage.Feature.Notifications)
logger = log.webview.debug if is_qtbug else log.webview.warning
logger("Ignoring feature permission {} for invalid URL {}".format(
permission_str, url))
deny_permission()
return
if feature not in self._options:
log.webview.error("Unhandled feature permission {}".format(
permission_str))
deny_permission()
return
if (
feature in [QWebEnginePage.Feature.DesktopVideoCapture,
QWebEnginePage.Feature.DesktopAudioVideoCapture] and
qtutils.version_check('5.13', compiled=False) and
not qtutils.version_check('5.13.2', compiled=False)
):
# WORKAROUND for https://bugreports.qt.io/browse/QTBUG-78016
log.webview.warning("Ignoring desktop sharing request due to "
"crashes in Qt < 5.13.2")
deny_permission()
return
question = shared.feature_permission(
url=url.adjusted(QUrl.UrlFormattingOption.RemovePath),
option=self._options[feature], msg=self._messages[feature],
yes_action=grant_permission, no_action=deny_permission,
abort_on=[self._tab.abort_questions])
if question is not None:
page.featurePermissionRequestCanceled.connect(
functools.partial(self._on_feature_permission_cancelled,
question, url, feature))
|
|
56,684 | 222,643 | 784 | python3.10.4/Lib/distutils/command/bdist_msi.py | 167 | 20 | def add_find_python(self):
start = 402
for ver in self.versions:
install_path = r"SOFTWARE\Python\PythonCore\%s\InstallPath" % ver
machine_reg = "python.machine." + ver
user_reg = "python.user." + ver
machine_prop = "PYTHON.MACHINE." + ver
user_prop = "PYTHON.USER." + ver
machine_action = "Pyth | add python 3.10.4 for windows | add_find_python | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | bdist_msi.py | 14 | 42 | https://github.com/XX-net/XX-Net.git | 3 | 304 | 0 | 86 | 469 | Python | {
"docstring": "Adds code to the installer to compute the location of Python.\n\n Properties PYTHON.MACHINE.X.Y and PYTHON.USER.X.Y will be set from the\n registry for each version of Python.\n\n Properties TARGETDIRX.Y will be set from PYTHON.USER.X.Y if defined,\n else from PYTHON.MACHINE.X.Y.\n\n Properties PYTHONX.Y will be set to TARGETDIRX.Y\\\\python.exe",
"language": "en",
"n_whitespaces": 79,
"n_words": 45,
"vocab_size": 28
} | def add_find_python(self):
start = 402
for ver in self.versions:
install_path = r"SOFTWARE\Python\PythonCore\%s\InstallPath" % ver
machine_reg = "python.machine." + ver
user_reg = "python.user." + ver
machine_prop = "PYTHON.MACHINE." + ver
user_prop = "PYTHON.USER." + ver
machine_action = "PythonFromMachine" + ver
user_action = "PythonFromUser" + ver
exe_action = "PythonExe" + ver
target_dir_prop = "TARGETDIR" + ver
exe_prop = "PYTHON" + ver
if msilib.Win64:
# type: msidbLocatorTypeRawValue + msidbLocatorType64bit
Type = 2+16
else:
Type = 2
add_data(self.db, "RegLocator",
[(machine_reg, 2, install_path, None, Type),
(user_reg, 1, install_path, None, Type)])
add_data(self.db, "AppSearch",
[(machine_prop, machine_reg),
(user_prop, user_reg)])
add_data(self.db, "CustomAction",
[(machine_action, 51+256, target_dir_prop, "[" + machine_prop + "]"),
(user_action, 51+256, target_dir_prop, "[" + user_prop + "]"),
(exe_action, 51+256, exe_prop, "[" + target_dir_prop + "]\\python.exe"),
])
add_data(self.db, "InstallExecuteSequence",
[(machine_action, machine_prop, start),
(user_action, user_prop, start + 1),
(exe_action, None, start + 2),
])
add_data(self.db, "InstallUISequence",
[(machine_action, machine_prop, start),
(user_action, user_prop, start + 1),
(exe_action, None, start + 2),
])
add_data(self.db, "Condition",
[("Python" + ver, 0, "NOT TARGETDIR" + ver)])
start += 4
assert start < 500
|
|
12,781 | 61,961 | 45 | .venv/lib/python3.8/site-packages/pip/_vendor/distlib/database.py | 13 | 8 | def write_exports(self, exports):
rf = self | upd; format | write_exports | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | transferlearning | database.py | 11 | 4 | https://github.com/jindongwang/transferlearning.git | 1 | 32 | 0 | 13 | 57 | Python | {
"docstring": "\n Write a dictionary of exports to a file in .ini format.\n :param exports: A dictionary of exports, mapping an export category to\n a list of :class:`ExportEntry` instances describing the\n individual export entries.\n ",
"language": "en",
"n_whitespaces": 100,
"n_words": 32,
"vocab_size": 25
} | def write_exports(self, exports):
rf = self.get_distinfo_file(EXPORTS_FILENAME)
with open(rf, 'w') as f:
write_exports(exports, f)
|
|
78,856 | 267,337 | 685 | lib/ansible/executor/task_executor.py | 191 | 41 | def _get_action_handler_with_module_context(self, connection, templar):
module_collection, separator, module_name = self._task.action.rpartition(".")
module_prefix = module_name.split('_')[0]
if module_collection:
# For network modules, which look for one action plugin per platform, look for the
# action plugin in the same collection as the module by prefixing the action plugin
# with the same collecti | Add toggle to fix module_defaults with module-as-redirected-action on a per-module basis (#77265)
* If there is a platform specific handler, prefer the resolved module over the resolved action when loading module_defaults
Add a toggle for action plugins to prefer the resolved module when loading module_defaults
Allow moving away from modules intercepted as actions pattern
Fixes #77059 | _get_action_handler_with_module_context | 621e782ed0c119d2c84124d006fdf253c082449a | ansible | task_executor.py | 15 | 38 | https://github.com/ansible/ansible.git | 8 | 264 | 0 | 117 | 420 | Python | {
"docstring": "\n Returns the correct action plugin to handle the requestion task action and the module context\n ",
"language": "en",
"n_whitespaces": 30,
"n_words": 15,
"vocab_size": 12
} | def _get_action_handler_with_module_context(self, connection, templar):
module_collection, separator, module_name = self._task.action.rpartition(".")
module_prefix = module_name.split('_')[0]
if module_collection:
# For network modules, which look for one action plugin per platform, look for the
# action plugin in the same collection as the module by prefixing the action plugin
# with the same collection.
network_action = "{0}.{1}".format(module_collection, module_prefix)
else:
network_action = module_prefix
collections = self._task.collections
# Check if the module has specified an action handler
module = self._shared_loader_obj.module_loader.find_plugin_with_context(
self._task.action, collection_list=collections
)
if not module.resolved or not module.action_plugin:
module = None
if module is not None:
handler_name = module.action_plugin
# let action plugin override module, fallback to 'normal' action plugin otherwise
elif self._shared_loader_obj.action_loader.has_plugin(self._task.action, collection_list=collections):
handler_name = self._task.action
elif all((module_prefix in C.NETWORK_GROUP_MODULES, self._shared_loader_obj.action_loader.has_plugin(network_action, collection_list=collections))):
handler_name = network_action
display.vvvv("Using network group action {handler} for {action}".format(handler=handler_name,
action=self._task.action),
host=self._play_context.remote_addr)
else:
# use ansible.legacy.normal to allow (historic) local action_plugins/ override without collections search
handler_name = 'ansible.legacy.normal'
collections = None # until then, we don't want the task's collection list to be consulted; use the builtin
handler = self._shared_loader_obj.action_loader.get(
handler_name,
task=self._task,
connection=connection,
play_context=self._play_context,
loader=self._loader,
templar=templar,
shared_loader_obj=self._shared_loader_obj,
collection_list=collections
)
if not handler:
raise AnsibleError("the handler '%s' was not found" % handler_name)
return handler, module
|
|
77,241 | 262,500 | 161 | TTS/tts/layers/losses.py | 61 | 18 | def forward(self, y_hat, y, length):
mask = sequence_mask(sequence_length=length, max_len=y.size(1)).unsqueeze(2)
y_norm = sample_wise_min_max(y, mask)
y_hat_norm = sample_wise_min_max(y_hat, mask)
ssim_loss = self.loss_func((y_norm * mask).unsqueeze(1), (y_hat_norm * mask).unsqueeze(1))
if ssim_loss.item() > 1.0:
print(f" > SSIM loss is out-of-range {ssim_loss.item()}, setting it 1.0")
ssim_loss == 1.0
if ssim_loss.item() < 0.0:
print(f" > SSIM loss is out-of-range {ssim_loss.item()}, setting it 0.0")
| Fix SSIM loss | forward | c17ff17a18f21be60c6916714ac8afd87d4441df | TTS | losses.py | 13 | 12 | https://github.com/coqui-ai/TTS.git | 3 | 122 | 0 | 40 | 203 | Python | {
"docstring": "\n Args:\n y_hat (tensor): model prediction values.\n y (tensor): target values.\n length (tensor): length of each sample in a batch for masking.\n\n Shapes:\n y_hat: B x T X D\n y: B x T x D\n length: B\n\n Returns:\n loss: An average loss value in range [0, 1] masked by the length.\n ",
"language": "en",
"n_whitespaces": 157,
"n_words": 50,
"vocab_size": 39
} | def forward(self, y_hat, y, length):
mask = sequence_mask(sequence_length=length, max_len=y.size(1)).unsqueeze(2)
y_norm = sample_wise_min_max(y, mask)
y_hat_norm = sample_wise_min_max(y_hat, mask)
ssim_loss = self.loss_func((y_norm * mask).unsqueeze(1), (y_hat_norm * mask).unsqueeze(1))
if ssim_loss.item() > 1.0:
print(f" > SSIM loss is out-of-range {ssim_loss.item()}, setting it 1.0")
ssim_loss == 1.0
if ssim_loss.item() < 0.0:
print(f" > SSIM loss is out-of-range {ssim_loss.item()}, setting it 0.0")
ssim_loss == 0.0
return ssim_loss
|
|
50,200 | 202,989 | 67 | django/core/management/__init__.py | 31 | 15 | def get_commands():
commands = {name: 'django.core' for name in find_commands(__path__[0])}
if not settings.configured:
return commands
for app_config in reversed(apps.get_app_configs()):
path = os.path.join(app_config.path, 'management')
commands.update({n | Refs #32355 -- Removed unnecessary list() calls before reversed() on dictviews.
Dict and dictviews are iterable in reversed insertion order using
reversed() in Python 3.8+. | get_commands | 7346c288e307e1821e3ceb757d686c9bd879389c | django | __init__.py | 13 | 8 | https://github.com/django/django.git | 5 | 77 | 0 | 22 | 126 | Python | {
"docstring": "\n Return a dictionary mapping command names to their callback applications.\n\n Look for a management.commands package in django.core, and in each\n installed application -- if a commands package exists, register all\n commands in that package.\n\n Core commands are always included. If a settings module has been\n specified, also include user-defined commands.\n\n The dictionary is in the format {command_name: app_name}. Key-value\n pairs from this dictionary can then be used in calls to\n load_command_class(app_name, command_name)\n\n If a specific version of a command must be loaded (e.g., with the\n startapp command), the instantiated module can be placed in the\n dictionary in place of the application name.\n\n The dictionary is cached on the first call and reused on subsequent\n calls.\n ",
"language": "en",
"n_whitespaces": 161,
"n_words": 115,
"vocab_size": 79
} | def get_commands():
commands = {name: 'django.core' for name in find_commands(__path__[0])}
if not settings.configured:
return commands
for app_config in reversed(apps.get_app_configs()):
path = os.path.join(app_config.path, 'management')
commands.update({name: app_config.name for name in find_commands(path)})
return commands
|
|
57,004 | 223,611 | 193 | python3.10.4/Lib/email/_parseaddr.py | 35 | 13 | def getphraselist(self):
plist = []
while self.pos < len(self.field):
if self.field[self.pos] in self.FWS:
self.pos += 1
elif self.field[self.pos] == '"':
plist.append(self.getquote())
elif self.field[self.pos] == '(':
s | add python 3.10.4 for windows | getphraselist | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | _parseaddr.py | 15 | 14 | https://github.com/XX-net/XX-Net.git | 6 | 119 | 0 | 26 | 196 | Python | {
"docstring": "Parse a sequence of RFC 2822 phrases.\n\n A phrase is a sequence of words, which are in turn either RFC 2822\n atoms or quoted-strings. Phrases are canonicalized by squeezing all\n runs of continuous whitespace into one space.\n ",
"language": "en",
"n_whitespaces": 66,
"n_words": 37,
"vocab_size": 30
} | def getphraselist(self):
plist = []
while self.pos < len(self.field):
if self.field[self.pos] in self.FWS:
self.pos += 1
elif self.field[self.pos] == '"':
plist.append(self.getquote())
elif self.field[self.pos] == '(':
self.commentlist.append(self.getcomment())
elif self.field[self.pos] in self.phraseends:
break
else:
plist.append(self.getatom(self.phraseends))
return plist
|
|
23,720 | 109,724 | 363 | lib/matplotlib/axes/_secondary_axes.py | 142 | 19 | def set_location(self, location):
# This puts the rectangle | Clean up code in SecondaryAxis | set_location | 8387676bc049d7b3e071846730c632e6ced137ed | matplotlib | _secondary_axes.py | 15 | 17 | https://github.com/matplotlib/matplotlib.git | 5 | 130 | 0 | 97 | 230 | Python | {
"docstring": "\n Set the vertical or horizontal location of the axes in\n parent-normalized coordinates.\n\n Parameters\n ----------\n location : {'top', 'bottom', 'left', 'right'} or float\n The position to put the secondary axis. Strings can be 'top' or\n 'bottom' for orientation='x' and 'right' or 'left' for\n orientation='y'. A float indicates the relative position on the\n parent axes to put the new axes, 0.0 being the bottom (or left)\n and 1.0 being the top (or right).\n ",
"language": "en",
"n_whitespaces": 170,
"n_words": 71,
"vocab_size": 51
} | def set_location(self, location):
# This puts the rectangle into figure-relative coordinates.
if isinstance(location, str):
_api.check_in_list(self._locstrings, location=location)
self._pos = 1. if location in ('top', 'right') else 0.
elif isinstance(location, numbers.Real):
self._pos = location
else:
raise ValueError(
f"location must be {self._locstrings[0]!r}, "
f"{self._locstrings[1]!r}, or a float, not {location!r}")
self._loc = location
if self._orientation == 'x':
# An x-secondary axes is like an inset axes from x = 0 to x = 1 and
# from y = pos to y = pos + eps, in the parent's transAxes coords.
bounds = [0, self._pos, 1., 1e-10]
else: # 'y'
bounds = [self._pos, 0, 1e-10, 1]
# this locator lets the axes move in the parent axes coordinates.
# so it never needs to know where the parent is explicitly in
# figure coordinates.
# it gets called in ax.apply_aspect() (of all places)
self.set_axes_locator(
_TransformedBoundsLocator(bounds, self._parent.transAxes))
|
|
35,383 | 153,347 | 149 | modin/core/execution/ray/implementations/pandas_on_ray/partitioning/partition.py | 24 | 14 | def length(self):
if self._length_cache is None:
if len(self.call_queue):
self.drain_call_queue()
else:
self._length_cache, self._width_cache = _get_index_and_columns.remote(
self.oid
| REFACTOR-#4251: define public interfaces in `modin.core.execution.ray` module (#3868)
Signed-off-by: Anatoly Myachev <anatoly.myachev@intel.com> | length | e7cb2e82f8b9c7a68f82abdd3b6011d661230b7e | modin | partition.py | 14 | 11 | https://github.com/modin-project/modin.git | 4 | 70 | 0 | 19 | 115 | Python | {
"docstring": "\n Get the length of the object wrapped by this partition.\n\n Returns\n -------\n int\n The length of the object.\n ",
"language": "en",
"n_whitespaces": 65,
"n_words": 18,
"vocab_size": 14
} | def length(self):
if self._length_cache is None:
if len(self.call_queue):
self.drain_call_queue()
else:
self._length_cache, self._width_cache = _get_index_and_columns.remote(
self.oid
)
if isinstance(self._length_cache, ObjectIDType):
self._length_cache = ray.get(self._length_cache)
return self._length_cache
|
|
47,480 | 195,939 | 44 | sympy/polys/densearith.py | 25 | 8 | def dmp_l2_norm_squared(f, u, K):
if not u:
return dup_l2_norm_squared(f, K)
v = u - 1
return s | Add `l2_norm_squared` methods. | dmp_l2_norm_squared | 0f6dde45a1c75b02c208323574bdb09b8536e3e4 | sympy | densearith.py | 10 | 5 | https://github.com/sympy/sympy.git | 3 | 44 | 0 | 23 | 67 | Python | {
"docstring": "\n Returns squared l2 norm of a polynomial in ``K[X]``.\n\n Examples\n ========\n\n >>> from sympy.polys import ring, ZZ\n >>> R, x,y = ring(\"x,y\", ZZ)\n\n >>> R.dmp_l2_norm_squared(2*x*y - x - 3)\n 14\n\n ",
"language": "en",
"n_whitespaces": 55,
"n_words": 30,
"vocab_size": 27
} | def dmp_l2_norm_squared(f, u, K):
if not u:
return dup_l2_norm_squared(f, K)
v = u - 1
return sum([ dmp_l2_norm_squared(c, v, K) for c in f ])
|
|
78,551 | 266,740 | 72 | test/lib/ansible_test/_internal/commands/integration/cloud/__init__.py | 40 | 9 | def cloud_filter(args, targets): # type: (IntegrationConfig, t.Tuple[IntegrationTarget, ...]) -> t.List[str]
if args.metadata.cloud_config is not None:
return [] # cloud filter already performed prior to delegation
exclude = [] # type: t.List[str]
for provider in get_cloud_providers( | ansible-test - Code cleanup and refactoring. (#77169)
* Remove unnecessary PyCharm ignores.
* Ignore intentional undefined attribute usage.
* Add missing type hints. Fix existing type hints.
* Fix docstrings and comments.
* Use function to register completion handler.
* Pass strings to display functions.
* Fix CompositeAction handling of dest argument.
* Use consistent types in expressions/assignments.
* Use custom function to keep linters happy.
* Add missing raise for custom exception.
* Clean up key/value type handling in cloud plugins.
* Use dataclass instead of dict for results.
* Add custom type_guard function to check lists.
* Ignore return type that can't be checked (yet).
* Avoid changing types on local variables. | cloud_filter | a06fa496d3f837cca3c437ab6e9858525633d147 | ansible | __init__.py | 9 | 7 | https://github.com/ansible/ansible.git | 3 | 45 | 0 | 32 | 74 | Python | {
"docstring": "Return a list of target names to exclude based on the given targets.",
"language": "en",
"n_whitespaces": 12,
"n_words": 13,
"vocab_size": 13
} | def cloud_filter(args, targets): # type: (IntegrationConfig, t.Tuple[IntegrationTarget, ...]) -> t.List[str]
if args.metadata.cloud_config is not None:
return [] # cloud filter already performed prior to delegation
exclude = [] # type: t.List[str]
for provider in get_cloud_providers(args, targets):
provider.filter(targets, exclude)
return exclude
|
|
53,805 | 215,087 | 252 | tests/pytests/unit/modules/test_aixpkg.py | 64 | 19 | def test_upgrade_available_none():
chk_upgrade_out = (
"Last metadata ex | Working tests for install | test_upgrade_available_none | f1c37893caf90738288e789c3233ab934630254f | salt | test_aixpkg.py | 16 | 21 | https://github.com/saltstack/salt.git | 1 | 124 | 0 | 56 | 217 | Python | {
"docstring": "\n test upgrade available where a valid upgrade is not available\n ",
"language": "en",
"n_whitespaces": 17,
"n_words": 10,
"vocab_size": 8
} | def test_upgrade_available_none():
chk_upgrade_out = (
"Last metadata expiration check: 22:5:48 ago on Mon Dec 6 19:26:36 EST 2021."
)
dnf_call = MagicMock(return_value={"retcode": 100, "stdout": chk_upgrade_out})
version_mock = MagicMock(return_value="6.6-2")
with patch("pathlib.Path.is_file", return_value=True):
with patch.dict(
aixpkg.__salt__,
{"cmd.run_all": dnf_call, "config.get": MagicMock(return_value=False)},
), patch.object(aixpkg, "version", version_mock):
result = aixpkg.upgrade_available("info")
assert dnf_call.call_count == 1
libpath_env = {"LIBPATH": "/opt/freeware/lib:/usr/lib"}
dnf_call.assert_any_call(
"/opt/freeware/bin/dnf check-update info",
env=libpath_env,
ignore_retcode=True,
python_shell=False,
)
assert not result
|
|
18,273 | 87,293 | 373 | tests/sentry/event_manager/test_event_manager.py | 56 | 27 | def test_too_many_boosted_releases_do_not_boost_anymore(self):
release_2 = Release.get_or_create( | feat(ds): Limit the amount of boosted releases to 10 (#40501)
Limits amount of boosted releases to 10 releases
otherwise do not add any more releases to hash set of listed releases | test_too_many_boosted_releases_do_not_boost_anymore | 361b7f444a53cc34cad8ddc378d125b7027d96df | sentry | test_event_manager.py | 14 | 27 | https://github.com/getsentry/sentry.git | 2 | 185 | 0 | 46 | 342 | Python | {
"docstring": "\n This test tests the case when we have already too many boosted releases, in this case we want to skip the\n boosting of anymore releases\n ",
"language": "en",
"n_whitespaces": 47,
"n_words": 25,
"vocab_size": 22
} | def test_too_many_boosted_releases_do_not_boost_anymore(self):
release_2 = Release.get_or_create(self.project, "2.0")
release_3 = Release.get_or_create(self.project, "3.0")
for release_id in (self.release.id, release_2.id):
self.redis_client.set(f"ds::p:{self.project.id}:r:{release_id}", 1, 60 * 60 * 24)
self.redis_client.hset(
f"ds::p:{self.project.id}:boosted_releases",
release_id,
time(),
)
with self.options(
{
"dynamic-sampling:boost-latest-release": True,
}
):
self.make_release_transaction(
release_version=release_3.version,
environment_name=self.environment1.name,
project_id=self.project.id,
checksum="b" * 32,
timestamp=self.timestamp,
)
assert self.redis_client.hgetall(f"ds::p:{self.project.id}:boosted_releases") == {
str(self.release.id): str(time()),
str(release_2.id): str(time()),
}
assert self.redis_client.get(f"ds::p:{self.project.id}:r:{release_3.id}") is None
|
|
41,745 | 176,175 | 175 | networkx/algorithms/link_analysis/hits_alg.py | 90 | 39 | def hits(G, max_iter=100, tol=1.0e-8, nstart=None, normalized=True):
import numpy as np
import scipy as sp
imp | Use scipy.sparse array datastructure (#5139)
* Step 1: use sparse arrays in nx.to_scipy_sparse_matrix.
Seems like a reasonable place to start.
nx.to_scipy_sparse_matrix is one of the primary interfaces to
scipy.sparse from within NetworkX.
* 1: Use np.outer instead of mult col/row vectors
Fix two instances in modularitymatrix where a new 2D array was being
created via an outer product of two \"vectors\".
In the matrix case, this was a row vector \* a column vector. In the
array case this can be disambiguated by being explicit with np.outer.
* Update _transition_matrix in laplacianmatrix module
- A few instances of matrix multiplication operator
- Add np.newaxis + transpose to get shape right for broadcasting
- Explicitly convert e.g. sp.sparse.spdiags to a csr_array.
* Update directed_combinitorial_laplacian w/ sparse array.
- Wrap spdiags in csr_array and update matmul operators.
* Rm matrix-specific code from lgc and hmn modules
- Replace .A call with appropriate array semantics
- wrap sparse.diags in csr_array.
* Change hits to use sparse array semantics.
- Replace * with @
- Remove superfluous calls to flatten.
* Update sparse matrix usage in layout module.
- Simplify lil.getrowview call
- Wrap spdiags in csr_array.
* lil_matrix -> lil_array in graphmatrix.py.
* WIP: Start working on algebraic connectivity module.
* Incorporate auth mat varname feedback.
* Revert 1D slice and comment for 1D sparse future.
* Add TODOs: rm csr_array wrapper around spdiags etc.
* WIP: cleanup algebraicconn: tracemin_fiedler.
* Typo.
* Finish reviewing algebraicconnectivity.
* Convert bethe_hessian matrix to use sparse arrays.
* WIP: update laplacian.
Update undirected laplacian functions.
* WIP: laplacian - add comment about _transition_matrix return types.
* Finish laplacianmatrix review.
* Update attrmatrix.
* Switch to official laplacian function.
* Update pagerank to use sparse array.
* Switch bipartite matrix to sparse arrays.
* Check from_scipy_sparse_matrix works with arrays.
Modifies test suite.
* Apply changes from review.
* Fix failing docstring tests.
* Fix missing axis for in-place multiplication.
* Use scipy==1.8rc2
* Use matrix multiplication
* Fix PyPy CI
* [MRG] Create plot_subgraphs.py example (#5165)
* Create plot_subgraphs.py
https://github.com/networkx/networkx/issues/4220
* Update plot_subgraphs.py
black
* Update plot_subgraphs.py
lint plus font_size
* Update plot_subgraphs.py
added more plots
* Update plot_subgraphs.py
removed plots from the unit test and added comments
* Update plot_subgraphs.py
lint
* Update plot_subgraphs.py
typos fixed
* Update plot_subgraphs.py
added nodes to the plot of the edges removed that was commented out for whatever reason
* Update plot_subgraphs.py
revert the latest commit - the line was commented out for a reason - it's broken
* Update plot_subgraphs.py
fixed node color issue
* Update plot_subgraphs.py
format fix
* Update plot_subgraphs.py
forgot to draw the nodes... now fixed
* Fix sphinx warnings about heading length.
* Update examples/algorithms/plot_subgraphs.py
* Update examples/algorithms/plot_subgraphs.py
Co-authored-by: Ross Barnowski <rossbar@berkeley.edu>
Co-authored-by: Dan Schult <dschult@colgate.edu>
* Add traveling salesman problem to example gallery (#4874)
Adds an example of the using Christofides to solve the TSP problem to the example galery.
Co-authored-by: Ross Barnowski <rossbar@berkeley.edu>
* Fixed inconsistent documentation for nbunch parameter in DiGraph.edges() (#5037)
* Fixed inconsistent documentation for nbunch parameter in DiGraph.edges()
* Resolved Requested Changes
* Revert changes to degree docstrings.
* Update comments in example.
* Apply wording to edges method in all graph classes.
Co-authored-by: Ross Barnowski <rossbar@berkeley.edu>
* Compatibility updates from testing with numpy/scipy/pytest rc's (#5226)
* Rm deprecated scipy subpkg access.
* Use recwarn fixture in place of deprecated pytest pattern.
* Rm unnecessary try/except from tests.
* Replace internal `close` fn with `math.isclose`. (#5224)
* Replace internal close fn with math.isclose.
* Fix lines in docstring examples.
* Fix Python 3.10 deprecation warning w/ int div. (#5231)
* Touchups and suggestions for subgraph gallery example (#5225)
* Simplify construction of G with edges rm'd
* Rm unused graph attribute.
* Shorten categorization by node type.
* Simplify node coloring.
* Simplify isomorphism check.
* Rm unit test.
* Rm redundant plotting of each subgraph.
* Use new package name (#5234)
* Allowing None edges in weight function of bidirectional Dijkstra (#5232)
* added following feature also to bidirectional dijkstra: The weight function can be used to hide edges by returning None.
* changed syntax for better readability and code duplicate avoidance
Co-authored-by: Hohmann, Nikolas <nikolas.hohmann@tu-darmstadt.de>
* Add an FAQ about assigning issues. (#5182)
* Add FAQ about assigning issues.
* Add note about linking issues from new PRs.
* Update dev deps (#5243)
* Update minor doc issues with tex notation (#5244)
* Add FutureWarnings to fns that return sparse matrices
- biadjacency_matrix.
- bethe_hessian_matrix.
- incidence_matrix.
- laplacian functions.
- modularity_matrix functions.
- adjacency_matrix.
* Add to_scipy_sparse_array and use it everywhere.
Add a new conversion function to preserve array semantics internally
while not altering behavior for users.
Also adds FutureWarning to to_scipy_sparse_matrix.
* Add from_scipy_sparse_array. Supercedes from_scipy_sparse_matrix.
* Handle deprecations in separate PR.
* Fix docstring examples.
Co-authored-by: Mridul Seth <mail@mriduls.com>
Co-authored-by: Jarrod Millman <jarrod.millman@gmail.com>
Co-authored-by: Andrew Knyazev <andrew.knyazev@ucdenver.edu>
Co-authored-by: Dan Schult <dschult@colgate.edu>
Co-authored-by: eskountis <56514439+eskountis@users.noreply.github.com>
Co-authored-by: Anutosh Bhat <87052487+anutosh491@users.noreply.github.com>
Co-authored-by: NikHoh <nikhoh@web.de>
Co-authored-by: Hohmann, Nikolas <nikolas.hohmann@tu-darmstadt.de>
Co-authored-by: Sultan Orazbayev <contact@econpoint.com>
Co-authored-by: Mridul Seth <mail@mriduls.com> | hits | 5dfd57af2a141a013ae3753e160180b82bec9469 | networkx | hits_alg.py | 15 | 20 | https://github.com/networkx/networkx.git | 4 | 226 | 0 | 56 | 339 | Python | {
"docstring": "Returns HITS hubs and authorities values for nodes.\n\n The HITS algorithm computes two numbers for a node.\n Authorities estimates the node value based on the incoming links.\n Hubs estimates the node value based on outgoing links.\n\n Parameters\n ----------\n G : graph\n A NetworkX graph\n\n max_iter : integer, optional\n Maximum number of iterations in power method.\n\n tol : float, optional\n Error tolerance used to check convergence in power method iteration.\n\n nstart : dictionary, optional\n Starting value of each node for power method iteration.\n\n normalized : bool (default=True)\n Normalize results by the sum of all of the values.\n\n Returns\n -------\n (hubs,authorities) : two-tuple of dictionaries\n Two dictionaries keyed by node containing the hub and authority\n values.\n\n Raises\n ------\n PowerIterationFailedConvergence\n If the algorithm fails to converge to the specified tolerance\n within the specified number of iterations of the power iteration\n method.\n\n Examples\n --------\n >>> G = nx.path_graph(4)\n >>> h, a = nx.hits(G)\n\n Notes\n -----\n The eigenvector calculation is done by the power iteration method\n and has no guarantee of convergence. The iteration will stop\n after max_iter iterations or an error tolerance of\n number_of_nodes(G)*tol has been reached.\n\n The HITS algorithm was designed for directed graphs but this\n algorithm does not check if the input graph is directed and will\n execute on undirected graphs.\n\n References\n ----------\n .. [1] A. Langville and C. Meyer,\n \"A survey of eigenvector methods of web information retrieval.\"\n http://citeseer.ist.psu.edu/713792.html\n .. [2] Jon Kleinberg,\n Authoritative sources in a hyperlinked environment\n Journal of the ACM 46 (5): 604-32, 1999.\n doi:10.1145/324133.324140.\n http://www.cs.cornell.edu/home/kleinber/auth.pdf.\n ",
"language": "en",
"n_whitespaces": 446,
"n_words": 248,
"vocab_size": 152
} | def hits(G, max_iter=100, tol=1.0e-8, nstart=None, normalized=True):
import numpy as np
import scipy as sp
import scipy.sparse.linalg # call as sp.sparse.linalg
if len(G) == 0:
return {}, {}
A = nx.adjacency_matrix(G, nodelist=list(G), dtype=float)
if nstart is None:
u, s, vt = sp.sparse.linalg.svds(A, k=1, maxiter=max_iter, tol=tol)
else:
nstart = np.array(list(nstart.values()))
u, s, vt = sp.sparse.linalg.svds(A, k=1, v0=nstart, maxiter=max_iter, tol=tol)
a = vt.flatten().real
h = A @ a
if normalized:
h = h / h.sum()
a = a / a.sum()
hubs = dict(zip(G, map(float, h)))
authorities = dict(zip(G, map(float, a)))
return hubs, authorities
|
|
8,731 | 45,823 | 87 | airflow/providers/ftp/hooks/ftp.py | 22 | 10 | def test_connection(self) -> Tuple[bool, str]:
try:
conn = se | Updates FTPHook provider to have test_connection (#21997)
* Updates FTP provider to have test_connection
Co-authored-by: eladkal <45845474+eladkal@users.noreply.github.com> | test_connection | 26e8d6d7664bbaae717438bdb41766550ff57e4f | airflow | ftp.py | 11 | 8 | https://github.com/apache/airflow.git | 2 | 41 | 0 | 21 | 71 | Python | {
"docstring": "Test the FTP connection by calling path with directory",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | def test_connection(self) -> Tuple[bool, str]:
try:
conn = self.get_conn()
conn.pwd
return True, "Connection successfully tested"
except Exception as e:
return False, str(e)
|
|
85,397 | 285,727 | 352 | openbb_terminal/cryptocurrency/crypto_controller.py | 74 | 28 | def call_price(self, other_args):
parser = argparse.ArgumentParser(
add_help=False,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
prog="price",
description=,
)
parser.add_argument(
"-s",
"--symbol",
required="-h" not in other_args,
type=str,
dest="symbol",
help="Symbol of coin to load data for, ~100 symbols are availa | Integrate live feeds from Pyth (#2178)
* added dependency
* added pyth models
* dependencies
* docs
* some improvements to this pyth command (#2433)
* some improvements to this pyth command
* minor improv
* dependencies
* tests
Co-authored-by: DidierRLopes <dro.lopes@campus.fct.unl.pt>; COlin | call_price | 1661ddd44044c637526e9a1e812e7c1863be35fc | OpenBBTerminal | crypto_controller.py | 13 | 26 | https://github.com/OpenBB-finance/OpenBBTerminal.git | 5 | 131 | 0 | 64 | 221 | Python | {
"docstring": "Process price commandDisplay price and interval of confidence in real-time. [Source: Pyth]",
"language": "en",
"n_whitespaces": 11,
"n_words": 12,
"vocab_size": 11
} | def call_price(self, other_args):
parser = argparse.ArgumentParser(
add_help=False,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
prog="price",
description=,
)
parser.add_argument(
"-s",
"--symbol",
required="-h" not in other_args,
type=str,
dest="symbol",
help="Symbol of coin to load data for, ~100 symbols are available",
)
if other_args and "-" not in other_args[0][0]:
other_args.insert(0, "-s")
ns_parser = self.parse_known_args_and_warn(parser, other_args)
if ns_parser:
if ns_parser.symbol in pyth_model.ASSETS.keys():
console.print(
"[param]If it takes too long, you can use 'Ctrl + C' to cancel.\n[/param]"
)
pyth_view.display_price(ns_parser.symbol)
else:
console.print("[red]The symbol selected does not exist.[/red]\n")
|
|
21,793 | 104,238 | 316 | src/datasets/utils/py_utils.py | 182 | 38 | def _single_map_nested(args):
function, data_struct, types, rank, disable_tqdm, desc = args
# Singleton first to spare some computation
if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
return function(data_struct)
# Reduce logging to keep things readable in multiprocessing with tqdm
if rank is not None and logging.get_verbosity() < logging.WARNING:
logging.set_verbosity_warning()
# Print at least one thing to fix tqdm in notebooks in multiprocessing
# see https://github.com/tqdm/tqdm/issues/485#issuecomment-473338308
if rank is not None and not disable_tqdm and any("notebook" in tqdm_cls.__name__ for tqdm_cls in tqdm.__mro__):
print(" ", end="", flush=True)
# Loop over single examples or batches and write to buffer/file if examples are to be updated
pbar_iterable = data_struct.items() if isinstance(data_struct, dict) else data_struct
pbar_desc = (desc + " " if desc is not None else "") + "#" + str(rank) if rank is not None else desc
pbar = utils.tqdm(pbar_iterable, dis | Better TQDM output (#3654)
* Show progress bar when generating examples
* Consistent utils.is_progress_bar_enabled calls
* Fix tqdm in notebook
* Add missing params to DatasetDict.map
* Specify total in tqdm progress bar in map
* Fix total computation
* Small fix
* Add desc to map_nested
* Add more precise descriptions to download
* Address comments
* Fix docstring
* Final changes
* Minor change | _single_map_nested | 6ed6ac9448311930557810383d2cfd4fe6aae269 | datasets | py_utils.py | 13 | 21 | https://github.com/huggingface/datasets.git | 17 | 259 | 0 | 107 | 398 | Python | {
"docstring": "Apply a function recursively to each element of a nested data struct.",
"language": "en",
"n_whitespaces": 11,
"n_words": 12,
"vocab_size": 11
} | def _single_map_nested(args):
function, data_struct, types, rank, disable_tqdm, desc = args
# Singleton first to spare some computation
if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
return function(data_struct)
# Reduce logging to keep things readable in multiprocessing with tqdm
if rank is not None and logging.get_verbosity() < logging.WARNING:
logging.set_verbosity_warning()
# Print at least one thing to fix tqdm in notebooks in multiprocessing
# see https://github.com/tqdm/tqdm/issues/485#issuecomment-473338308
if rank is not None and not disable_tqdm and any("notebook" in tqdm_cls.__name__ for tqdm_cls in tqdm.__mro__):
print(" ", end="", flush=True)
# Loop over single examples or batches and write to buffer/file if examples are to be updated
pbar_iterable = data_struct.items() if isinstance(data_struct, dict) else data_struct
pbar_desc = (desc + " " if desc is not None else "") + "#" + str(rank) if rank is not None else desc
pbar = utils.tqdm(pbar_iterable, disable=disable_tqdm, position=rank, unit="obj", desc=pbar_desc)
if isinstance(data_struct, dict):
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
else:
mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]
if isinstance(data_struct, list):
return mapped
elif isinstance(data_struct, tuple):
return tuple(mapped)
else:
return np.array(mapped)
|
|
51,930 | 207,334 | 99 | tests/admin_scripts/tests.py | 35 | 11 | def test_unified(self):
| Refs #33476 -- Reformatted code with Black. | test_unified | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | django | tests.py | 11 | 9 | https://github.com/django/django.git | 1 | 77 | 0 | 26 | 140 | Python | {
"docstring": "--output=unified emits settings diff in unified mode.",
"language": "en",
"n_whitespaces": 6,
"n_words": 7,
"vocab_size": 7
} | def test_unified(self):
self.write_settings("settings_to_diff.py", sdict={"FOO": '"bar"'})
args = ["diffsettings", "--settings=settings_to_diff", "--output=unified"]
out, err = self.run_manage(args)
self.assertNoOutput(err)
self.assertOutput(out, "+ FOO = 'bar'")
self.assertOutput(out, "- SECRET_KEY = ''")
self.assertOutput(out, "+ SECRET_KEY = 'django_tests_secret_key'")
self.assertNotInOutput(out, " APPEND_SLASH = True")
|
|
31,793 | 139,848 | 18 | python/ray/runtime_context.py | 4 | 5 | def runtime_env(self):
| [runtime env] runtime env inheritance refactor (#24538)
* [runtime env] runtime env inheritance refactor (#22244)
Runtime Environments is already GA in Ray 1.6.0. The latest doc is [here](https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments). And now, we already supported a [inheritance](https://docs.ray.io/en/master/ray-core/handling-dependencies.html#inheritance) behavior as follows (copied from the doc):
- The runtime_env["env_vars"] field will be merged with the runtime_env["env_vars"] field of the parent. This allows for environment variables set in the parent’s runtime environment to be automatically propagated to the child, even if new environment variables are set in the child’s runtime environment.
- Every other field in the runtime_env will be overridden by the child, not merged. For example, if runtime_env["py_modules"] is specified, it will replace the runtime_env["py_modules"] field of the parent.
We think this runtime env merging logic is so complex and confusing to users because users can't know the final runtime env before the jobs are run.
Current PR tries to do a refactor and change the behavior of Runtime Environments inheritance. Here is the new behavior:
- **If there is no runtime env option when we create actor, inherit the parent runtime env.**
- **Otherwise, use the optional runtime env directly and don't do the merging.**
Add a new API named `ray.runtime_env.get_current_runtime_env()` to get the parent runtime env and modify this dict by yourself. Like:
```Actor.options(runtime_env=ray.runtime_env.get_current_runtime_env().update({"X": "Y"}))```
This new API also can be used in ray client. | runtime_env | eb2692cb32bb1747e312d5b20e976d7a879c9588 | ray | runtime_context.py | 9 | 2 | https://github.com/ray-project/ray.git | 1 | 17 | 0 | 4 | 31 | Python | {
"docstring": "Get the runtime env of the current job/worker.\n\n If this API is called in driver or ray client, returns the job level runtime\n env.\n If this API is called in workers/actors, returns the worker level runtime env.\n\n Returns:\n A new ray.runtime_env.RuntimeEnv instance.\n\n To merge from the current runtime env in some specific cases, you can get the\n current runtime env by this API and modify it by yourself.\n\n Example:\n >>> # Inherit current runtime env, except `env_vars`\n >>> Actor.options( # doctest: +SKIP\n ... runtime_env=ray.get_runtime_context().runtime_env.update(\n ... {\"env_vars\": {\"A\": \"a\", \"B\": \"b\"}})\n ... ) # doctest: +SKIP\n ",
"language": "en",
"n_whitespaces": 205,
"n_words": 95,
"vocab_size": 60
} | def runtime_env(self):
return RuntimeEnv.deserialize(self._get_runtime_env_string())
|
|
45,770 | 187,407 | 65 | src/streamlink/stream/dash.py | 19 | 7 | def sleeper(self, duration):
s = time()
yield
time_to_sleep = duration - (time() - s)
if time_to_sleep > 0:
s | stream.dash: update DASHStreamWorker.iter_segments
- Refactor DASHStreamWorker.iter_segments()
- Replace dash_manifest.sleeper() with SegmentedStreamWorker.wait(),
and make the worker thread shut down immediately on close().
- Prevent unnecessary wait times for static manifest types by calling
close() after all segments were put into the writer's queue. | sleeper | d1a8d1597d4fe9f129a72fe94c1508304b7eae0f | streamlink | dash.py | 11 | 6 | https://github.com/streamlink/streamlink.git | 2 | 36 | 0 | 16 | 63 | Python | {
"docstring": "\n Do something and then wait for a given duration minus the time it took doing something\n ",
"language": "en",
"n_whitespaces": 31,
"n_words": 16,
"vocab_size": 15
} | def sleeper(self, duration):
s = time()
yield
time_to_sleep = duration - (time() - s)
if time_to_sleep > 0:
self.wait(time_to_sleep)
|
|
23,566 | 109,399 | 1,100 | lib/matplotlib/tests/test_colors.py | 623 | 52 | def test_BoundaryNorm():
boundaries = [0, 1.1, 2.2]
vals = [-1, 0, 1, 2, 2.2, 4]
# Without interpolation
expected = [-1, 0, 0, 1, 2, 2]
ncolors = len(boundaries) - 1
bn = mcolors.BoundaryNorm(boundaries, ncolors)
assert_array_equal(bn(vals), expected)
# ncolors != len(boundaries) - 1 triggers interpolation
expected = [-1, 0, 0, 2, 3, 3]
ncolors = len(boundaries)
bn = mcolors.BoundaryNorm(boundaries, ncolors)
assert_array_equal(bn(vals), expected)
# with a single region and interpolation
expected = [-1, 1, 1, 1, 3, 3]
bn = mcolors.BoundaryNorm([0, 2.2], ncolors)
assert_array_equal(bn(vals), expected)
# more boundaries for a third color
boundaries = [0, 1, 2, 3]
vals = [-1, 0.1, 1.1, 2.2, 4]
ncolors = 5
expected = [-1, 0, 2, 4, 5]
bn = mcolors.BoundaryNorm(boundaries, ncolors)
assert_array_equal(bn(vals), expected)
# a scalar as input should not trigger an error and should return a scalar
boundaries = [0, 1, 2]
vals = [-1, 0.1, 1.1, 2.2]
bn = mcolors.BoundaryNorm(boundaries, 2)
expected = [-1, 0, 1, 2]
for v, ex in zip(vals, expected):
ret = bn(v)
assert isinstance(ret, int)
assert_array_equal(ret, ex)
assert_array_equal(bn([v]), ex)
# same with interp
bn = mcolors.BoundaryNorm(boundaries, 3)
expected = [-1, 0, 2, 3]
for v, ex in zip(vals, expected):
ret = bn(v)
assert isinstance(ret, int)
assert_array_equal(ret, ex)
assert_array_equal(bn([v]), ex)
# Clipping
bn = mcolors.BoundaryNorm(boundaries, 3, clip=True)
expected = [0, 0, 2, 2]
for v, ex in zip(vals, expected):
ret = bn(v)
assert isinstance(ret, int)
assert_array_equal(ret, ex)
assert_array_equal(bn([v]), ex)
# Masked arrays
boundaries = [0, 1.1, 2.2]
vals = np.ma.masked_invalid([-1., np.NaN, 0, 1.4, 9])
# Without interpolation
ncolors = len(boundaries) - 1
bn = mcolors.BoundaryNorm(boundaries, ncolors)
expected = np.ma.masked_array([-1, -99, 0, 1, 2], mask=[0, 1, 0, 0, 0])
assert_array_equal(bn(vals), expected)
# With interpolation
bn = mcolors.BoundaryNorm(boundaries, len(boundaries))
expected = np.ma.masked_array([-1, -99, 0, 2, 3], mask=[0, 1, 0, 0, 0])
assert_array_equal(bn(vals), expected)
# Non-trivial masked arrays
vals = np.ma.masked_invalid([np.Inf, np.NaN])
assert np.all(bn(vals).mask)
vals = np.ma.masked_invalid([np.Inf])
assert np.all(bn(vals).mask)
# Incompatible extend and clip
with pytest.raises(ValueError, match="not compatible"):
mcolors.BoundaryNorm(np.arange(4), 5, extend='both', clip=True)
# Too small ncolors argument
with pytest.raises(ValueError, match="ncolors must equal or exceed"):
mcolors.BoundaryNorm(np.arange(4), 2)
with pytest.raises(ValueError, match="ncolors must equal or exceed"):
mcolors.BoundaryNorm(np.arange(4), 3, extend='min')
with pytest.raises(ValueError, match="ncolors must equal or exceed"):
mcolors.BoundaryNorm(np.arange(4), 4, extend='both')
# Testing extend keyword, with interpolation (large cmap)
bounds = [1, 2, 3]
cmap = mpl.colormaps['viridis']
mynorm = mcolors.BoundaryNorm(bounds, cmap.N, extend='both')
refnorm = mcolors.BoundaryNorm([0] + bounds + [4], cmap.N)
x = np.random.randn(100) * 10 + 2
ref = refnorm(x)
ref[ref == 0] = -1
ref[ref == cmap.N - 1] = cmap.N
assert_array_equal(mynorm(x), ref)
# Without interpolation
cmref = mcolors.ListedColormap(['blue', 'red'])
cmref.set_over('black')
cmref.set_under('white')
cmshould = mcolors.ListedColormap(['white', 'blue', 'red', 'black'])
assert mcolors.same_color(cmref.get_over(), 'black')
assert mcolors.same_color(cmref.get_under(), 'white')
refnorm = mcolors.BoundaryNorm(bounds, cmref.N)
mynorm = mcolors.BoundaryNorm(bounds, cmshould.N, extend='both')
assert mynorm.vmin == refnorm.vmin
assert mynorm.vmax == refnorm.vmax
assert mynorm(bounds[0] - 0.1) == -1 # under
assert mynorm(bounds[0] + 0.1) == 1 # first bin -> second color
assert mynorm(bounds[-1] - 0.1) == cmshould.N - 2 # next-to-last color
assert mynorm(bounds[-1] + 0.1) == cmshould.N # over
x = [-1, 1.2, 2.3, 9.6]
assert_array_equal(cmshould(mynorm(x)), cmshould([0, 1, 2, 3]))
x = np.random.randn(100) * 10 + 2
assert_array_equal(cmshould(mynorm(x)), cmref(refnorm(x)))
# Just min
cmref = mcolors.ListedColormap(['blue', 'red'])
cmref.set_under('white')
cmshould = mcolors.ListedColormap(['white', 'blue', 'red'])
assert mcolors.same_color(cmref.get_under(), 'white')
assert cmref.N == 2
assert cmshould.N == 3
refnorm = mcolors.BoundaryNorm(bounds, cmref.N)
mynorm = mcolors.BoundaryNorm(bounds, cmshould.N, extend='min')
assert mynorm.vmin == refnorm.vmin
assert mynorm.vmax == refnorm.vmax
x = [-1, 1.2, 2.3]
assert_array_equal(cmshould(mynorm(x)), cmshould([0, 1, 2]))
x = np.random.randn(100) * 10 + 2
assert_array_equal(cmshould(mynorm(x)), cmref(refnorm(x)))
# Just max
cmref = mcolors.Lis | MNT: convert tests and internal usage way from using mpl.cm.get_cmap | test_BoundaryNorm | a17f4f3bd63e3ca3754f96d7db4ce5197720589b | matplotlib | test_colors.py | 12 | 119 | https://github.com/matplotlib/matplotlib.git | 4 | 1,470 | 0 | 192 | 2,192 | Python | {
"docstring": "\n GitHub issue #1258: interpolation was failing with numpy\n 1.7 pre-release.\n ",
"language": "en",
"n_whitespaces": 20,
"n_words": 10,
"vocab_size": 10
} | def test_BoundaryNorm():
boundaries = [0, 1.1, 2.2]
vals = [-1, 0, 1, 2, 2.2, 4]
# Without interpolation
expected = [-1, 0, 0, 1, 2, 2]
ncolors = len(boundaries) - 1
bn = mcolors.BoundaryNorm(boundaries, ncolors)
assert_array_equal(bn(vals), expected)
# ncolors != len(boundaries) - 1 triggers interpolation
expected = [-1, 0, 0, 2, 3, 3]
ncolors = len(boundaries)
bn = mcolors.BoundaryNorm(boundaries, ncolors)
assert_array_equal(bn(vals), expected)
# with a single region and interpolation
expected = [-1, 1, 1, 1, 3, 3]
bn = mcolors.BoundaryNorm([0, 2.2], ncolors)
assert_array_equal(bn(vals), expected)
# more boundaries for a third color
boundaries = [0, 1, 2, 3]
vals = [-1, 0.1, 1.1, 2.2, 4]
ncolors = 5
expected = [-1, 0, 2, 4, 5]
bn = mcolors.BoundaryNorm(boundaries, ncolors)
assert_array_equal(bn(vals), expected)
# a scalar as input should not trigger an error and should return a scalar
boundaries = [0, 1, 2]
vals = [-1, 0.1, 1.1, 2.2]
bn = mcolors.BoundaryNorm(boundaries, 2)
expected = [-1, 0, 1, 2]
for v, ex in zip(vals, expected):
ret = bn(v)
assert isinstance(ret, int)
assert_array_equal(ret, ex)
assert_array_equal(bn([v]), ex)
# same with interp
bn = mcolors.BoundaryNorm(boundaries, 3)
expected = [-1, 0, 2, 3]
for v, ex in zip(vals, expected):
ret = bn(v)
assert isinstance(ret, int)
assert_array_equal(ret, ex)
assert_array_equal(bn([v]), ex)
# Clipping
bn = mcolors.BoundaryNorm(boundaries, 3, clip=True)
expected = [0, 0, 2, 2]
for v, ex in zip(vals, expected):
ret = bn(v)
assert isinstance(ret, int)
assert_array_equal(ret, ex)
assert_array_equal(bn([v]), ex)
# Masked arrays
boundaries = [0, 1.1, 2.2]
vals = np.ma.masked_invalid([-1., np.NaN, 0, 1.4, 9])
# Without interpolation
ncolors = len(boundaries) - 1
bn = mcolors.BoundaryNorm(boundaries, ncolors)
expected = np.ma.masked_array([-1, -99, 0, 1, 2], mask=[0, 1, 0, 0, 0])
assert_array_equal(bn(vals), expected)
# With interpolation
bn = mcolors.BoundaryNorm(boundaries, len(boundaries))
expected = np.ma.masked_array([-1, -99, 0, 2, 3], mask=[0, 1, 0, 0, 0])
assert_array_equal(bn(vals), expected)
# Non-trivial masked arrays
vals = np.ma.masked_invalid([np.Inf, np.NaN])
assert np.all(bn(vals).mask)
vals = np.ma.masked_invalid([np.Inf])
assert np.all(bn(vals).mask)
# Incompatible extend and clip
with pytest.raises(ValueError, match="not compatible"):
mcolors.BoundaryNorm(np.arange(4), 5, extend='both', clip=True)
# Too small ncolors argument
with pytest.raises(ValueError, match="ncolors must equal or exceed"):
mcolors.BoundaryNorm(np.arange(4), 2)
with pytest.raises(ValueError, match="ncolors must equal or exceed"):
mcolors.BoundaryNorm(np.arange(4), 3, extend='min')
with pytest.raises(ValueError, match="ncolors must equal or exceed"):
mcolors.BoundaryNorm(np.arange(4), 4, extend='both')
# Testing extend keyword, with interpolation (large cmap)
bounds = [1, 2, 3]
cmap = mpl.colormaps['viridis']
mynorm = mcolors.BoundaryNorm(bounds, cmap.N, extend='both')
refnorm = mcolors.BoundaryNorm([0] + bounds + [4], cmap.N)
x = np.random.randn(100) * 10 + 2
ref = refnorm(x)
ref[ref == 0] = -1
ref[ref == cmap.N - 1] = cmap.N
assert_array_equal(mynorm(x), ref)
# Without interpolation
cmref = mcolors.ListedColormap(['blue', 'red'])
cmref.set_over('black')
cmref.set_under('white')
cmshould = mcolors.ListedColormap(['white', 'blue', 'red', 'black'])
assert mcolors.same_color(cmref.get_over(), 'black')
assert mcolors.same_color(cmref.get_under(), 'white')
refnorm = mcolors.BoundaryNorm(bounds, cmref.N)
mynorm = mcolors.BoundaryNorm(bounds, cmshould.N, extend='both')
assert mynorm.vmin == refnorm.vmin
assert mynorm.vmax == refnorm.vmax
assert mynorm(bounds[0] - 0.1) == -1 # under
assert mynorm(bounds[0] + 0.1) == 1 # first bin -> second color
assert mynorm(bounds[-1] - 0.1) == cmshould.N - 2 # next-to-last color
assert mynorm(bounds[-1] + 0.1) == cmshould.N # over
x = [-1, 1.2, 2.3, 9.6]
assert_array_equal(cmshould(mynorm(x)), cmshould([0, 1, 2, 3]))
x = np.random.randn(100) * 10 + 2
assert_array_equal(cmshould(mynorm(x)), cmref(refnorm(x)))
# Just min
cmref = mcolors.ListedColormap(['blue', 'red'])
cmref.set_under('white')
cmshould = mcolors.ListedColormap(['white', 'blue', 'red'])
assert mcolors.same_color(cmref.get_under(), 'white')
assert cmref.N == 2
assert cmshould.N == 3
refnorm = mcolors.BoundaryNorm(bounds, cmref.N)
mynorm = mcolors.BoundaryNorm(bounds, cmshould.N, extend='min')
assert mynorm.vmin == refnorm.vmin
assert mynorm.vmax == refnorm.vmax
x = [-1, 1.2, 2.3]
assert_array_equal(cmshould(mynorm(x)), cmshould([0, 1, 2]))
x = np.random.randn(100) * 10 + 2
assert_array_equal(cmshould(mynorm(x)), cmref(refnorm(x)))
# Just max
cmref = mcolors.ListedColormap(['blue', 'red'])
cmref.set_over('black')
cmshould = mcolors.ListedColormap(['blue', 'red', 'black'])
assert mcolors.same_color(cmref.get_over(), 'black')
assert cmref.N == 2
assert cmshould.N == 3
refnorm = mcolors.BoundaryNorm(bounds, cmref.N)
mynorm = mcolors.BoundaryNorm(bounds, cmshould.N, extend='max')
assert mynorm.vmin == refnorm.vmin
assert mynorm.vmax == refnorm.vmax
x = [1.2, 2.3, 4]
assert_array_equal(cmshould(mynorm(x)), cmshould([0, 1, 2]))
x = np.random.randn(100) * 10 + 2
assert_array_equal(cmshould(mynorm(x)), cmref(refnorm(x)))
|
|
36,066 | 154,556 | 912 | modin/experimental/core/execution/native/implementations/hdk_on_native/dataframe/dataframe.py | 171 | 44 | def _join_by_index(self, other_modin_frames, how, sort, ignore_index):
if how == "outer":
raise NotImplementedError("outer join is not supported in HDK engine")
lhs = self._maybe_materialize_rowid()
reset_index_names = False
for rhs in other_modin_frames:
rhs = rhs._maybe_materialize_rowid()
if len(lhs._index_cols) != len(rhs._index_cols):
raise NotImplementedError(
"join by indexes with different sizes is not supported"
)
reset_index_names = reset_index_names or lhs._index_cols != rhs._index_cols
condition = lhs._build_equi_join_condition(
rhs, lhs._index_cols, rhs._index_cols
)
exprs = lhs._index_exprs()
new_columns = lhs.columns.to_list()
for col in lhs.columns:
e | FEAT-#4946: Replace OmniSci with HDK (#4947)
Co-authored-by: Iaroslav Igoshev <Poolliver868@mail.ru>
Signed-off-by: Andrey Pavlenko <andrey.a.pavlenko@gmail.com> | _join_by_index | e5b1888cd932909e49194d58035da34b210b91c4 | modin | dataframe.py | 16 | 57 | https://github.com/modin-project/modin.git | 11 | 315 | 0 | 113 | 498 | Python | {
"docstring": "\n Perform equi-join operation for multiple frames by index columns.\n\n Parameters\n ----------\n other_modin_frames : list of HdkOnNativeDataframe\n Frames to join with.\n how : str\n A type of join.\n sort : bool\n Sort the result by join keys.\n ignore_index : bool\n If True then reset column index for the resulting frame.\n\n Returns\n -------\n HdkOnNativeDataframe\n The new frame.\n ",
"language": "en",
"n_whitespaces": 188,
"n_words": 55,
"vocab_size": 43
} | def _join_by_index(self, other_modin_frames, how, sort, ignore_index):
if how == "outer":
raise NotImplementedError("outer join is not supported in HDK engine")
lhs = self._maybe_materialize_rowid()
reset_index_names = False
for rhs in other_modin_frames:
rhs = rhs._maybe_materialize_rowid()
if len(lhs._index_cols) != len(rhs._index_cols):
raise NotImplementedError(
"join by indexes with different sizes is not supported"
)
reset_index_names = reset_index_names or lhs._index_cols != rhs._index_cols
condition = lhs._build_equi_join_condition(
rhs, lhs._index_cols, rhs._index_cols
)
exprs = lhs._index_exprs()
new_columns = lhs.columns.to_list()
for col in lhs.columns:
exprs[col] = lhs.ref(col)
for col in rhs.columns:
# Handle duplicating column names here. When user specifies
# suffixes to make a join, actual renaming is done in front-end.
new_col_name = col
rename_idx = 0
while new_col_name in exprs:
new_col_name = f"{col}{rename_idx}"
rename_idx += 1
exprs[new_col_name] = rhs.ref(col)
new_columns.append(new_col_name)
op = JoinNode(
lhs,
rhs,
how=how,
exprs=exprs,
condition=condition,
)
new_columns = Index.__new__(
Index, data=new_columns, dtype=self.columns.dtype
)
lhs = lhs.__constructor__(
dtypes=lhs._dtypes_for_exprs(exprs),
columns=new_columns,
index_cols=lhs._index_cols,
op=op,
force_execution_mode=self._force_execution_mode,
)
if sort:
lhs = lhs.sort_rows(
lhs._index_cols,
ascending=True,
ignore_index=False,
na_position="last",
)
if reset_index_names:
lhs = lhs._reset_index_names()
if ignore_index:
new_columns = Index.__new__(RangeIndex, data=range(len(lhs.columns)))
lhs = lhs._set_columns(new_columns)
return lhs
|
|
342 | 2,710 | 112 | packages/syft/src/syft/core/node/common/action/function_or_constructor_action.py | 25 | 16 | def _object2proto(self) -> RunFunctionOrConstructorAction_PB:
return RunFunctionOrConstructorAction_PB(
path=self.path,
args=[serialize(x, to_bytes=True) for x in self.args],
kwargs={k: serialize(v, to_bytes=True) for k, v in self.kwargs.items()},
id_at_location=serialize(self.id_at_location),
address=serialize(self.address),
msg_id=serialize(s | [syft.core.node.common.action] Change syft import absolute -> relative | _object2proto | e272ed2fa4c58e0a89e273a3e85da7d13a85e04c | PySyft | function_or_constructor_action.py | 13 | 23 | https://github.com/OpenMined/PySyft.git | 3 | 91 | 0 | 22 | 135 | Python | {
"docstring": "Returns a protobuf serialization of self.\n\n As a requirement of all objects which inherit from Serializable,\n this method transforms the current object into the corresponding\n Protobuf object so that it can be further serialized.\n\n :return: returns a protobuf object\n :rtype: RunFunctionOrConstructorAction_PB\n\n .. note::\n This method is purely an internal method. Please use serialize(object) or one of\n the other public serialization methods if you wish to serialize an\n object.\n ",
"language": "en",
"n_whitespaces": 150,
"n_words": 68,
"vocab_size": 56
} | def _object2proto(self) -> RunFunctionOrConstructorAction_PB:
return RunFunctionOrConstructorAction_PB(
path=self.path,
args=[serialize(x, to_bytes=True) for x in self.args],
kwargs={k: serialize(v, to_bytes=True) for k, v in self.kwargs.items()},
id_at_location=serialize(self.id_at_location),
address=serialize(self.address),
msg_id=serialize(self.id),
)
|
|
41,741 | 176,171 | 370 | networkx/generators/small.py | 56 | 5 | def truncated_cube_graph(create_using=None):
description = [
"adjacencylist",
"Truncated Cube Graph",
24,
[
[2, 3, 5],
[12, 15],
[4, 5],
[7, 9],
[6],
[17, 19],
[8, 9],
[11, 13],
[10],
[18, 21],
[12, 13],
[15],
[14],
[22, 23],
[16],
[20, 24],
[18, 19],
[21],
[20],
[24],
[22],
[23],
[24],
[],
],
| Docstrings for the small.py module (#5240)
* added description for the first 5 small graphs
* modified descriptions based on comment and added description for two more functions
* added doctrings to all the functions
* Minor touchups.
Co-authored-by: Ross Barnowski <rossbar@berkeley.edu> | truncated_cube_graph | dec723f072eb997a497a159dbe8674cd39999ee9 | networkx | small.py | 9 | 34 | https://github.com/networkx/networkx.git | 1 | 152 | 0 | 46 | 197 | Python | {
"docstring": "\n Returns the skeleton of the truncated cube.\n\n The truncated cube is an Archimedean solid with 14 regular\n faces (6 octagonal and 8 triangular), 36 edges and 24 nodes [1]_.\n The truncated cube is created by truncating (cutting off) the tips\n of the cube one third of the way into each edge [2]_.\n\n Parameters\n ----------\n create_using : NetworkX graph constructor, optional (default=nx.Graph)\n Graph type to create. If graph instance, then cleared before populated.\n\n Returns\n -------\n G : networkx Graph\n Skeleton of the truncated cube\n\n References\n ----------\n .. [1] https://en.wikipedia.org/wiki/Truncated_cube\n .. [2] https://www.coolmath.com/reference/polyhedra-truncated-cube\n\n ",
"language": "en",
"n_whitespaces": 153,
"n_words": 91,
"vocab_size": 68
} | def truncated_cube_graph(create_using=None):
description = [
"adjacencylist",
"Truncated Cube Graph",
24,
[
[2, 3, 5],
[12, 15],
[4, 5],
[7, 9],
[6],
[17, 19],
[8, 9],
[11, 13],
[10],
[18, 21],
[12, 13],
[15],
[14],
[22, 23],
[16],
[20, 24],
[18, 19],
[21],
[20],
[24],
[22],
[23],
[24],
[],
],
]
G = make_small_undirected_graph(description, create_using)
return G
|
|
15,593 | 70,994 | 53 | wagtail/contrib/modeladmin/options.py | 14 | 5 | def get_admin_urls_for_registration(self):
urls = ()
for instance in self.modeladmin_instances:
urls += instance.get_admin_urls_for_registration()
return urls
| Fix warnings from flake8-comprehensions. | get_admin_urls_for_registration | de3fcba9e95818e9634ab7de6bfcb1f4221f2775 | wagtail | options.py | 10 | 5 | https://github.com/wagtail/wagtail.git | 2 | 26 | 0 | 12 | 45 | Python | {
"docstring": "\n Utilised by Wagtail's 'register_admin_urls' hook to register urls for\n used by any associated ModelAdmin instances\n ",
"language": "en",
"n_whitespaces": 37,
"n_words": 15,
"vocab_size": 14
} | def get_admin_urls_for_registration(self):
urls = ()
for instance in self.modeladmin_instances:
urls += instance.get_admin_urls_for_registration()
return urls
|
|
13,241 | 63,304 | 63 | .venv/lib/python3.8/site-packages/pip/_vendor/pyparsing.py | 17 | 7 | def setName(self, name):
self.name = name
self.errmsg = "Expected " + self.name
if __diag__.enable_debug_on_named_expressions:
self.setDebug()
return self
| upd; format | setName | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | transferlearning | pyparsing.py | 9 | 6 | https://github.com/jindongwang/transferlearning.git | 2 | 34 | 0 | 15 | 59 | Python | {
"docstring": "\n Define name for this expression, makes debugging and exception messages clearer.\n\n Example::\n\n Word(nums).parseString(\"ABC\") # -> Exception: Expected W:(0123...) (at char 0), (line:1, col:1)\n Word(nums).setName(\"integer\").parseString(\"ABC\") # -> Exception: Expected integer (at char 0), (line:1, col:1)\n ",
"language": "en",
"n_whitespaces": 80,
"n_words": 34,
"vocab_size": 25
} | def setName(self, name):
self.name = name
self.errmsg = "Expected " + self.name
if __diag__.enable_debug_on_named_expressions:
self.setDebug()
return self
|
|
35,257 | 153,097 | 82 | modin/core/dataframe/algebra/default2pandas/groupby.py | 21 | 5 | def get_func(cls, key, **kwargs):
if "agg_func" in kwargs:
return cls.inplace_applyier_builder(key, kwargs["agg_func"])
elif "func_dict" in kwargs:
return cls.inplace_applyier_builder(key, kwargs["func_dict"])
else:
return cls.inplace_applyier_builder(key)
| FIX-#3197: do not pass lambdas to the backend in GroupBy (#3373)
Signed-off-by: Dmitry Chigarev <dmitry.chigarev@intel.com> | get_func | 1e65a4afd191cf61ba05b80545d23f9b88962f41 | modin | groupby.py | 12 | 7 | https://github.com/modin-project/modin.git | 3 | 54 | 0 | 16 | 92 | Python | {
"docstring": "\n Extract aggregation function from groupby arguments.\n\n Parameters\n ----------\n key : callable or str\n Default aggregation function. If aggregation function is not specified\n via groupby arguments, then `key` function is used.\n **kwargs : dict\n GroupBy arguments that may contain aggregation function.\n\n Returns\n -------\n callable\n Aggregation function.\n\n Notes\n -----\n There are two ways of how groupby aggregation can be invoked:\n 1. Explicitly with query compiler method: `qc.groupby_sum()`.\n 2. By passing aggregation function as an argument: `qc.groupby_agg(\"sum\")`.\n Both are going to produce the same result, however in the first case actual aggregation\n function can be extracted from the method name, while for the second only from the method arguments.\n ",
"language": "en",
"n_whitespaces": 271,
"n_words": 106,
"vocab_size": 78
} | def get_func(cls, key, **kwargs):
if "agg_func" in kwargs:
return cls.inplace_applyier_builder(key, kwargs["agg_func"])
elif "func_dict" in kwargs:
return cls.inplace_applyier_builder(key, kwargs["func_dict"])
else:
return cls.inplace_applyier_builder(key)
|
|
29,983 | 133,351 | 44 | python/ray/util/sgd/torch/torch_trainer.py | 12 | 9 | def update_scheduler(self, metric):
self.worker_group.apply_all_operators(
lambda op: [sched.step(m | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | update_scheduler | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | ray | torch_trainer.py | 11 | 4 | https://github.com/ray-project/ray.git | 2 | 32 | 0 | 12 | 52 | Python | {
"docstring": "Calls ``scheduler.step(metric)`` on all registered schedulers.\n\n This is useful for lr_schedulers such as ``ReduceLROnPlateau``.\n ",
"language": "en",
"n_whitespaces": 28,
"n_words": 14,
"vocab_size": 14
} | def update_scheduler(self, metric):
self.worker_group.apply_all_operators(
lambda op: [sched.step(metric) for sched in op._schedulers]
)
|
|
75,273 | 258,521 | 56 | sklearn/metrics/pairwise.py | 31 | 10 | def paired_cosine_distances(X, Y):
X, Y = c | DOC Ensures that sklearn.metrics.pairwise.paired_cosine_distances passes numpydoc validation (#22141)
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> | paired_cosine_distances | a5b70b3132467b5e3616178d9ecca6cb7316c400 | scikit-learn | pairwise.py | 11 | 3 | https://github.com/scikit-learn/scikit-learn.git | 1 | 39 | 0 | 27 | 108 | Python | {
"docstring": "\n Compute the paired cosine distances between X and Y.\n\n Read more in the :ref:`User Guide <metrics>`.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n An array where each row is a sample and each column is a feature.\n\n Y : array-like of shape (n_samples, n_features)\n An array where each row is a sample and each column is a feature.\n\n Returns\n -------\n distances : ndarray of shape (n_samples,)\n Returns the distances between the row vectors of `X`\n and the row vectors of `Y`, where `distances[i]` is the\n distance between `X[i]` and `Y[i]`.\n\n Notes\n -----\n The cosine distance is equivalent to the half the squared\n euclidean distance if each sample is normalized to unit norm.\n ",
"language": "en",
"n_whitespaces": 192,
"n_words": 114,
"vocab_size": 57
} | def paired_cosine_distances(X, Y):
X, Y = check_paired_arrays(X, Y)
return 0.5 * row_norms(normalize(X) - normalize(Y), squared=True)
PAIRED_DISTANCES = {
"cosine": paired_cosine_distances,
"euclidean": paired_euclidean_distances,
"l2": paired_euclidean_distances,
"l1": paired_manhattan_distances,
"manhattan": paired_manhattan_distances,
"cityblock": paired_manhattan_distances,
}
|
|
5,648 | 30,695 | 131 | src/transformers/trainer.py | 29 | 12 | def torchdynamo_smart_context_manager(self):
ctx_manager = contextlib.nullcontext()
if is_torchdynamo_available():
import torchdynamo
from torchdy | Support compilation via Torchdynamo, AOT Autograd, NVFuser (#17308)
* Support compilation via Torchdynamo, AOT Autograd, NVFuser
* Address comments
* Lint
* Stas comments - missing quality test
* Lintere
* Quality test
* Doc lint
* Reset CUDA peak mem
* Add CustomTrainer
* require a single gpu
Co-authored-by: Stas Bekman <stas@stason.org> | torchdynamo_smart_context_manager | 897a8dd89f40817201bc4aebe532a096405bdeb1 | transformers | trainer.py | 13 | 10 | https://github.com/huggingface/transformers.git | 4 | 64 | 0 | 20 | 112 | Python | {
"docstring": "\n A helper wrapper that creates an appropriate context manager for `torchdynamo`.\n ",
"language": "en",
"n_whitespaces": 26,
"n_words": 11,
"vocab_size": 11
} | def torchdynamo_smart_context_manager(self):
ctx_manager = contextlib.nullcontext()
if is_torchdynamo_available():
import torchdynamo
from torchdynamo.optimizations.training import aot_autograd_speedup_strategy
if self.args.torchdynamo == "eager":
ctx_manager = torchdynamo.optimize("eager")
elif self.args.torchdynamo == "nvfuser":
ctx_manager = torchdynamo.optimize(aot_autograd_speedup_strategy)
return ctx_manager
|
|
45,584 | 186,677 | 110 | certbot-apache/certbot_apache/_internal/parser.py | 20 | 9 | def check_aug_version(self) -> bool:
self.aug.set("/test/path/testing/arg", "aRgUMeNT")
try:
matches = self.aug.match(
"/test//*[self::arg=~regexp('argument', 'i')]")
except RuntimeError:
self.aug.remove("/test/path")
return False
self.aug.remove("/test/path")
return matches
| Add typing to certbot.apache (#9071)
* Add typing to certbot.apache
Co-authored-by: Adrien Ferrand <ferrand.ad@gmail.com> | check_aug_version | 7d9e9a49005de7961e84d2a7c608db57dbab3046 | certbot | parser.py | 11 | 13 | https://github.com/certbot/certbot.git | 2 | 53 | 0 | 17 | 98 | Python | {
"docstring": " Checks that we have recent enough version of libaugeas.\n If augeas version is recent enough, it will support case insensitive\n regexp matching",
"language": "en",
"n_whitespaces": 36,
"n_words": 22,
"vocab_size": 20
} | def check_aug_version(self) -> bool:
self.aug.set("/test/path/testing/arg", "aRgUMeNT")
try:
matches = self.aug.match(
"/test//*[self::arg=~regexp('argument', 'i')]")
except RuntimeError:
self.aug.remove("/test/path")
return False
self.aug.remove("/test/path")
return matches
|
|
37,003 | 157,635 | 42 | ldm/modules/midas/utils.py | 20 | 13 | def resize_depth(depth, width, height):
depth = torch.squeeze(depth[0, :, :, :]).to("cpu")
depth_resized = cv2.resize(
depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC
)
return depth_resized
| release more models | resize_depth | ca86da3a30c4e080d4db8c25fca73de843663cb4 | stablediffusion | utils.py | 12 | 6 | https://github.com/Stability-AI/stablediffusion.git | 1 | 58 | 0 | 17 | 91 | Python | {
"docstring": "Resize depth map and bring to CPU (numpy).\n\n Args:\n depth (tensor): depth\n width (int): image width\n height (int): image height\n\n Returns:\n array: processed depth\n ",
"language": "en",
"n_whitespaces": 61,
"n_words": 24,
"vocab_size": 17
} | def resize_depth(depth, width, height):
depth = torch.squeeze(depth[0, :, :, :]).to("cpu")
depth_resized = cv2.resize(
depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC
)
return depth_resized
|
|
47,440 | 195,853 | 729 | sympy/core/numbers.py | 213 | 34 | def comp(z1, z2, tol=None):
r
if type(z2) is str:
if not | Improved documentation formatting | comp | cda8dfe6f45dc5ed394c2f5cda706cd6c729f713 | sympy | numbers.py | 24 | 105 | https://github.com/sympy/sympy.git | 26 | 381 | 0 | 107 | 605 | Python | {
"docstring": "Return a bool indicating whether the error between z1 and z2\n is $\\le$ ``tol``.\n\n Examples\n ========\n\n If ``tol`` is ``None`` then ``True`` will be returned if\n :math:`|z1 - z2|\\times 10^p \\le 5` where $p$ is minimum value of the\n decimal precision of each value.\n\n >>> from sympy import comp, pi\n >>> pi4 = pi.n(4); pi4\n 3.142\n >>> comp(_, 3.142)\n True\n >>> comp(pi4, 3.141)\n False\n >>> comp(pi4, 3.143)\n False\n\n A comparison of strings will be made\n if ``z1`` is a Number and ``z2`` is a string or ``tol`` is ''.\n\n >>> comp(pi4, 3.1415)\n True\n >>> comp(pi4, 3.1415, '')\n False\n\n When ``tol`` is provided and $z2$ is non-zero and\n :math:`|z1| > 1` the error is normalized by :math:`|z1|`:\n\n >>> abs(pi4 - 3.14)/pi4\n 0.000509791731426756\n >>> comp(pi4, 3.14, .001) # difference less than 0.1%\n True\n >>> comp(pi4, 3.14, .0005) # difference less than 0.1%\n False\n\n When :math:`|z1| \\le 1` the absolute error is used:\n\n >>> 1/pi4\n 0.3183\n >>> abs(1/pi4 - 0.3183)/(1/pi4)\n 3.07371499106316e-5\n >>> abs(1/pi4 - 0.3183)\n 9.78393554684764e-6\n >>> comp(1/pi4, 0.3183, 1e-5)\n True\n\n To see if the absolute error between ``z1`` and ``z2`` is less\n than or equal to ``tol``, call this as ``comp(z1 - z2, 0, tol)``\n or ``comp(z1 - z2, tol=tol)``:\n\n >>> abs(pi4 - 3.14)\n 0.00160156249999988\n >>> comp(pi4 - 3.14, 0, .002)\n True\n >>> comp(pi4 - 3.14, 0, .001)\n False\n ",
"language": "en",
"n_whitespaces": 363,
"n_words": 217,
"vocab_size": 120
} | def comp(z1, z2, tol=None):
r
if type(z2) is str:
if not pure_complex(z1, or_real=True):
raise ValueError('when z2 is a str z1 must be a Number')
return str(z1) == z2
if not z1:
z1, z2 = z2, z1
if not z1:
return True
if not tol:
a, b = z1, z2
if tol == '':
return str(a) == str(b)
if tol is None:
a, b = sympify(a), sympify(b)
if not all(i.is_number for i in (a, b)):
raise ValueError('expecting 2 numbers')
fa = a.atoms(Float)
fb = b.atoms(Float)
if not fa and not fb:
# no floats -- compare exactly
return a == b
# get a to be pure_complex
for _ in range(2):
ca = pure_complex(a, or_real=True)
if not ca:
if fa:
a = a.n(prec_to_dps(min([i._prec for i in fa])))
ca = pure_complex(a, or_real=True)
break
else:
fa, fb = fb, fa
a, b = b, a
cb = pure_complex(b)
if not cb and fb:
b = b.n(prec_to_dps(min([i._prec for i in fb])))
cb = pure_complex(b, or_real=True)
if ca and cb and (ca[1] or cb[1]):
return all(comp(i, j) for i, j in zip(ca, cb))
tol = 10**prec_to_dps(min(a._prec, getattr(b, '_prec', a._prec)))
return int(abs(a - b)*tol) <= 5
diff = abs(z1 - z2)
az1 = abs(z1)
if z2 and az1 > 1:
return diff/az1 <= tol
else:
return diff <= tol
|
|
70,677 | 245,152 | 491 | mmdet/datasets/openimages.py | 58 | 24 | def _parse_img_level_ann(self, image_level_ann_file):
item_lists = defaultdict(list)
with self.file_client.get_local_path(
image_level_ann_file) as local_path:
with open(local_path, 'r') as f:
reader = csv.reader(f)
i = -1
for line | Refactor OpenImages. | _parse_img_level_ann | 36c1f477b273cb2fb0dea3c921ec267db877c039 | mmdetection | openimages.py | 19 | 23 | https://github.com/open-mmlab/mmdetection.git | 3 | 122 | 0 | 45 | 201 | Python | {
"docstring": "Parse image level annotations from csv style ann_file.\n\n Args:\n image_level_ann_file (str): CSV style image level annotation\n file path.\n\n Returns:\n defaultdict[list[dict]]: Annotations where item of the defaultdict\n indicates an image, each of which has (n) dicts.\n Keys of dicts are:\n\n - `image_level_label` (int): of shape 1.\n - `confidence` (float): of shape 1.\n ",
"language": "en",
"n_whitespaces": 161,
"n_words": 51,
"vocab_size": 41
} | def _parse_img_level_ann(self, image_level_ann_file):
item_lists = defaultdict(list)
with self.file_client.get_local_path(
image_level_ann_file) as local_path:
with open(local_path, 'r') as f:
reader = csv.reader(f)
i = -1
for line in reader:
i += 1
if i == 0:
continue
else:
img_id = line[0]
label_id = line[1]
assert label_id in self.label_id_mapping
image_level_label = int(
self.label_id_mapping[label_id])
confidence = float(line[2])
item_lists[img_id].append(
dict(
image_level_label=image_level_label,
confidence=confidence))
return item_lists
|
|
55,789 | 219,771 | 32 | python3.10.4/Lib/_pydecimal.py | 11 | 7 | def logical_and(self, a, b):
a = _convert | add python 3.10.4 for windows | logical_and | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | _pydecimal.py | 9 | 3 | https://github.com/XX-net/XX-Net.git | 1 | 31 | 0 | 11 | 48 | Python | {
"docstring": "Applies the logical operation 'and' between each operand's digits.\n\n The operands must be both logical numbers.\n\n >>> ExtendedContext.logical_and(Decimal('0'), Decimal('0'))\n Decimal('0')\n >>> ExtendedContext.logical_and(Decimal('0'), Decimal('1'))\n Decimal('0')\n >>> ExtendedContext.logical_and(Decimal('1'), Decimal('0'))\n Decimal('0')\n >>> ExtendedContext.logical_and(Decimal('1'), Decimal('1'))\n Decimal('1')\n >>> ExtendedContext.logical_and(Decimal('1100'), Decimal('1010'))\n Decimal('1000')\n >>> ExtendedContext.logical_and(Decimal('1111'), Decimal('10'))\n Decimal('10')\n >>> ExtendedContext.logical_and(110, 1101)\n Decimal('100')\n >>> ExtendedContext.logical_and(Decimal(110), 1101)\n Decimal('100')\n >>> ExtendedContext.logical_and(110, Decimal(1101))\n Decimal('100')\n ",
"language": "en",
"n_whitespaces": 192,
"n_words": 52,
"vocab_size": 33
} | def logical_and(self, a, b):
a = _convert_other(a, raiseit=True)
return a.logical_and(b, context=self)
|
|
48,514 | 197,371 | 587 | sympy/utilities/enumerative.py | 182 | 16 | def decrement_part_small(self, part, ub):
if self.lpart >= ub - 1:
self.p1 += 1 # increment to keep track of usefulness of tests
return False
plen = len(part)
for j in range(plen - 1, -1, -1):
# Knuth's mod, (answer to problem 7.2.1.5.69)
if j == 0 and (part[0].v - 1)*(ub - self.lpart) < part[0].u:
self.k1 += 1
return False
if j == 0 and part[j].v > 1 or | Remove abbreviations in documentation | decrement_part_small | 65be461082dda54c8748922f9c29a19af1279fe1 | sympy | enumerative.py | 18 | 21 | https://github.com/sympy/sympy.git | 13 | 214 | 0 | 114 | 333 | Python | {
"docstring": "Decrements part (a subrange of pstack), if possible, returning\n True iff the part was successfully decremented.\n\n Parameters\n ==========\n\n part\n part to be decremented (topmost part on the stack)\n\n ub\n the maximum number of parts allowed in a partition\n returned by the calling traversal.\n\n Notes\n =====\n\n The goal of this modification of the ordinary decrement method\n is to fail (meaning that the subtree rooted at this part is to\n be skipped) when it can be proved that this part can only have\n child partitions which are larger than allowed by ``ub``. If a\n decision is made to fail, it must be accurate, otherwise the\n enumeration will miss some partitions. But, it is OK not to\n capture all the possible failures -- if a part is passed that\n should not be, the resulting too-large partitions are filtered\n by the enumeration one level up. However, as is usual in\n constrained enumerations, failing early is advantageous.\n\n The tests used by this method catch the most common cases,\n although this implementation is by no means the last word on\n this problem. The tests include:\n\n 1) ``lpart`` must be less than ``ub`` by at least 2. This is because\n once a part has been decremented, the partition\n will gain at least one child in the spread step.\n\n 2) If the leading component of the part is about to be\n decremented, check for how many parts will be added in\n order to use up the unallocated multiplicity in that\n leading component, and fail if this number is greater than\n allowed by ``ub``. (See code for the exact expression.) This\n test is given in the answer to Knuth's problem 7.2.1.5.69.\n\n 3) If there is *exactly* enough room to expand the leading\n component by the above test, check the next component (if\n it exists) once decrementing has finished. If this has\n ``v == 0``, this next component will push the expansion over the\n limit by 1, so fail.\n ",
"language": "en",
"n_whitespaces": 637,
"n_words": 319,
"vocab_size": 181
} | def decrement_part_small(self, part, ub):
if self.lpart >= ub - 1:
self.p1 += 1 # increment to keep track of usefulness of tests
return False
plen = len(part)
for j in range(plen - 1, -1, -1):
# Knuth's mod, (answer to problem 7.2.1.5.69)
if j == 0 and (part[0].v - 1)*(ub - self.lpart) < part[0].u:
self.k1 += 1
return False
if j == 0 and part[j].v > 1 or j > 0 and part[j].v > 0:
# found val to decrement
part[j].v -= 1
# Reset trailing parts back to maximum
for k in range(j + 1, plen):
part[k].v = part[k].u
# Have now decremented part, but are we doomed to
# failure when it is expanded? Check one oddball case
# that turns out to be surprisingly common - exactly
# enough room to expand the leading component, but no
# room for the second component, which has v=0.
if (plen > 1 and part[1].v == 0 and
(part[0].u - part[0].v) ==
((ub - self.lpart - 1) * part[0].v)):
self.k2 += 1
self.db_trace("Decrement fails test 3")
return False
return True
return False
|
|
27,935 | 125,638 | 40 | python/ray/runtime_context.py | 12 | 8 | def get_node_id(self) -> str:
node_id = self.worker.current_node_id
assert not node_id.is_nil()
return node_i | Ray 2.0 API deprecation (#26116)
Ray 2.0 API deprecation for:
ray.remote(): placement_group
ray.remote(): placement_group_bundle_index
ray.remote(): placement_group_capture_child_tasks
ray.get_dashboard_url()
ray.get_resource_ids()
ray.disconnect()
ray.connect()
ray.util.ActorGroup
ray.util.ActorPool
Add get_xx_id() to return hex (rather than object), and then deprecate the xx_id() (which returns Cython object): the xx here can be node, task etc.
ray start: --plasma-store-socket-name
ray start: --raylet-socket-name | get_node_id | 90cea203befa8f2e86e9c1c18bb3972296358e7b | ray | runtime_context.py | 8 | 12 | https://github.com/ray-project/ray.git | 1 | 28 | 0 | 12 | 49 | Python | {
"docstring": "Get current node ID for this worker or driver.\n\n Node ID is the id of a node that your driver, task, or actor runs.\n The ID will be in hex format.\n\n Returns:\n A node id in hex format for this worker or driver.\n ",
"language": "en",
"n_whitespaces": 82,
"n_words": 43,
"vocab_size": 30
} | def get_node_id(self) -> str:
node_id = self.worker.current_node_id
assert not node_id.is_nil()
return node_id.hex()
|
|
117,392 | 320,849 | 57 | qutebrowser/completion/models/configmodel.py | 16 | 10 | def list_option(*, info):
return _option(
info,
"List options",
lambda opt: (isinstance(info.config.get_obj(op | pylint: Fix new unnecessary-lambda-assignment | list_option | 6c4e2810285af0698538aed9d46a99de085eb310 | qutebrowser | configmodel.py | 15 | 7 | https://github.com/qutebrowser/qutebrowser.git | 2 | 41 | 0 | 16 | 67 | Python | {
"docstring": "A CompletionModel filled with settings whose values are lists.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | def list_option(*, info):
return _option(
info,
"List options",
lambda opt: (isinstance(info.config.get_obj(opt.name), list) and
not opt.no_autoconfig)
)
|
|
@pytest.mark.parametrize(
"query, fields",
[
(
"""
SELecT
campaign.id,
campaign.name,
campaign.status,
metrics.impressions FROM campaign
wheRe campaign.status = 'PAUSED'
AND metrics.impressions > 100
order by campaign.status
""",
["campaign.id", "campaign.name", "campaign.status", "metrics.impressions"],
),
(
"""
SELECT
campaign.accessible_bidding_strategy,
segments.ad_destination_type,
campaign.start_date,
campaign.end_date
FROM campaign
""",
["campaign.accessible_bidding_strategy", "segments.ad_destination_type", "campaign.start_date", "campaign.end_date"],
),
("""selet aasdasd from aaa""", []),
],
) | 480 | 3,546 | 201 | airbyte-integrations/connectors/source-google-ads/unit_tests/test_source.py | 53 | 18 | def get_instance_from_config_with_end_date(config, query):
start_date = "2021-03-04"
end_date = "2021-04-04"
conversion_window_days = 14
google_api = GoogleAds(credentials=config["credentials"], customer_id=config["customer_id"])
instance = CustomQuery(
api=google_api,
conversion_window_days=conversion_window_days,
start_date=start_date,
end_date=end_date,
time_zone="local",
custom_query_config={"query": query, "table_name": "whatever_table"},
)
return instance
@pytest.mark.parametrize(
"query, | Source GoogleAds: add end_date to config (#8669)
* GoogleAds add end_date to config
* Update script following review comments
* Add unit test
* Solve conflicts
* Solve conflicts in MR
* Update test_google_ads.py
Instanciate IncrementalGoogleAdsStream in tests + add missing line between functions
* Update test_source.py
remove extra hashtag
* Update tests with missing params
* Add missing time_zone param
* merge user code
* run format
* revert unit test stream count
* remove error log file
* bump connector version
* run seed file
Co-authored-by: Marcos Marx <marcosmarxm@gmail.com> | get_instance_from_config_with_end_date | 2e7ee756eb1d55080d707cef63454788a7abb6be | airbyte | test_source.py | 12 | 14 | https://github.com/airbytehq/airbyte.git | 1 | 73 | 1 | 44 | 208 | Python | {
"docstring": "\n SELecT\n campaign.id,\n campaign.name,\n campaign.status,\n metrics.impressions FROM campaign\nwheRe campaign.status = 'PAUSED'\nAND metrics.impressions > 100\norder by campaign.status\n \n SELECT\n campaign.accessible_bidding_strategy,\n segments.ad_destination_type,\n campaign.start_date,\n campaign.end_date\n FROM campaign\n selet aasdasd from aaa",
"language": "en",
"n_whitespaces": 98,
"n_words": 29,
"vocab_size": 25
} | def get_instance_from_config_with_end_date(config, query):
start_date = "2021-03-04"
end_date = "2021-04-04"
conversion_window_days = 14
google_api = GoogleAds(credentials=config["credentials"], customer_id=config["customer_id"])
instance = CustomQuery(
api=google_api,
conversion_window_days=conversion_window_days,
start_date=start_date,
end_date=end_date,
time_zone="local",
custom_query_config={"query": query, "table_name": "whatever_table"},
)
return instance
@pytest.mark.parametrize(
"query, fields",
[
(
,
["campaign.id", "campaign.name", "campaign.status", "metrics.impressions"],
),
(
,
["campaign.accessible_bidding_strategy", "segments.ad_destination_type", "campaign.start_date", "campaign.end_date"],
),
(, []),
],
) |
41,838 | 176,324 | 144 | networkx/algorithms/assortativity/pairs.py | 69 | 21 | def node_degree_xy(G, x="out", y="in", weight=None, nodes=None):
nodes = set(G) if nodes is None else set(nodes)
if G.is_directed():
direction = {"out": G.out_degree, "in": G.in_degree}
xdeg = direction[x]
ydeg = direction[y]
else:
xdeg = ydeg = G.degree
for u, degu in xdeg(nodes, weight=weight):
# use G.edges to treat multigraphs correctly
neighbors = (nbr for _, nbr in G.edges(u) if nbr in nodes)
fo | MAINT: Cleanup assortativity module, remove unused variables (#5301)
Remove unused variables, sort imports,
raise errors instead of accepting invalid arguments silently
Co-authored-by: Dan Schult <dschult@colgate.edu> | node_degree_xy | 34d9d630bb02426d297d3e20fedb7da8c3ced03a | networkx | pairs.py | 12 | 12 | https://github.com/networkx/networkx.git | 7 | 132 | 0 | 49 | 209 | Python | {
"docstring": "Generate node degree-degree pairs for edges in G.\n\n Parameters\n ----------\n G: NetworkX graph\n\n x: string ('in','out')\n The degree type for source node (directed graphs only).\n\n y: string ('in','out')\n The degree type for target node (directed graphs only).\n\n weight: string or None, optional (default=None)\n The edge attribute that holds the numerical value used\n as a weight. If None, then each edge has weight 1.\n The degree is the sum of the edge weights adjacent to the node.\n\n nodes: list or iterable (optional)\n Use only edges that are adjacency to specified nodes.\n The default is all nodes.\n\n Returns\n -------\n (x, y): 2-tuple\n Generates 2-tuple of (degree, degree) values.\n\n\n Examples\n --------\n >>> G = nx.DiGraph()\n >>> G.add_edge(1, 2)\n >>> list(nx.node_degree_xy(G, x=\"out\", y=\"in\"))\n [(1, 1)]\n >>> list(nx.node_degree_xy(G, x=\"in\", y=\"out\"))\n [(0, 0)]\n\n Notes\n -----\n For undirected graphs each edge is produced twice, once for each edge\n representation (u, v) and (v, u), with the exception of self-loop edges\n which only appear once.\n ",
"language": "en",
"n_whitespaces": 281,
"n_words": 157,
"vocab_size": 111
} | def node_degree_xy(G, x="out", y="in", weight=None, nodes=None):
nodes = set(G) if nodes is None else set(nodes)
if G.is_directed():
direction = {"out": G.out_degree, "in": G.in_degree}
xdeg = direction[x]
ydeg = direction[y]
else:
xdeg = ydeg = G.degree
for u, degu in xdeg(nodes, weight=weight):
# use G.edges to treat multigraphs correctly
neighbors = (nbr for _, nbr in G.edges(u) if nbr in nodes)
for _, degv in ydeg(neighbors, weight=weight):
yield degu, degv
|
|
29,985 | 133,353 | 88 | python/ray/util/sgd/torch/torch_trainer.py | 20 | 9 | def validate(self, num_steps=None, profile=False, reduce_results=True, info=None):
worker_stats = self.worker_group.validate(
| [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | validate | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | ray | torch_trainer.py | 9 | 8 | https://github.com/ray-project/ray.git | 2 | 56 | 0 | 18 | 85 | Python | {
"docstring": "Evaluates the model on the validation data set.\n\n Args:\n num_steps (int): Number of batches to compute update steps on\n per worker. This corresponds also to the number of times\n ``TrainingOperator.validate_batch`` is called per worker.\n profile (bool): Returns time stats for the evaluation procedure.\n reduce_results (bool): Whether to average all metrics across\n all workers into one dict. If a metric is a non-numerical\n value (or nested dictionaries), one value will be randomly\n selected among the workers. If False, returns a list of dicts.\n info (dict): Optional dictionary passed to the training\n operator for `validate` and `validate_batch`.\n\n Returns:\n A dictionary of metrics for validation.\n You can provide custom metrics by passing in a custom\n ``training_operator_cls``.\n ",
"language": "en",
"n_whitespaces": 309,
"n_words": 113,
"vocab_size": 84
} | def validate(self, num_steps=None, profile=False, reduce_results=True, info=None):
worker_stats = self.worker_group.validate(
num_steps=num_steps, profile=profile, info=info
)
if reduce_results:
return self._process_stats(worker_stats)
else:
return worker_stats
|
|
12,047 | 60,255 | 29 | code/deep/BJMMD/caffe/python/caffe/io.py | 8 | 6 | def set_raw_scale(self, in_, scale):
self.__check_input(in_)
self.raw_scale[in_] = scale
| Balanced joint maximum mean discrepancy for deep transfer learning | set_raw_scale | cc4d0564756ca067516f71718a3d135996525909 | transferlearning | io.py | 8 | 3 | https://github.com/jindongwang/transferlearning.git | 1 | 24 | 0 | 8 | 39 | Python | {
"docstring": "\n Set the scale of raw features s.t. the input blob = input * scale.\n While Python represents images in [0, 1], certain Caffe models\n like CaffeNet and AlexNet represent images in [0, 255] so the raw_scale\n of these models must be 255.\n\n Parameters\n ----------\n in_ : which input to assign this scale factor\n scale : scale coefficient\n ",
"language": "en",
"n_whitespaces": 121,
"n_words": 57,
"vocab_size": 44
} | def set_raw_scale(self, in_, scale):
self.__check_input(in_)
self.raw_scale[in_] = scale
|
|
30,178 | 134,046 | 238 | ci/run/bazel_sharding/tests/test_bazel_sharding.py | 151 | 16 | def test_add_rule_to_best_shard():
# If we start with an empty list, then add to first shard
shards: List[List[bazel_sharding.BazelRule]] = [list() for _ in range(4)]
optimum = 600
rule = bazel_sharding.BazelRule("mock", "medium")
bazel_sharding.add_rule_to_best_shard(rule, shards, optimum)
assert shards[0][0] == rule
assert all(not shard for shard in shards[1:])
# Add to first shard below optimum
old_rule = bazel_sharding.BazelRule("mock", "medium")
shards: List[List[bazel_sharding.BazelRule]] = [[old_rule] for _ in range(4)]
shards[3] = []
optimum = old_rule.actual_timeout_s
rule = bazel_sharding.BazelRule("mock", "small")
bazel_sharding.add_rule_to_best_shard(rule, shards, optimum)
assert shards[3][0] == rule
assert all(shard[-1] == old_rule for shard in shards[0:3])
# If all shards are above or equal optimum, add to the one with the smallest
# difference
old_rule = bazel_sharding.BazelRule("mock", "large")
shards: List[List[bazel_sharding.BazelRule]] = [[old_rule] for _ in range(4)]
optimum = old_rule.actual_timeout_s
old_rule_medium = bazel_sharding.BazelRule("mock", "medium")
shards[3][0] = old_rule_medium
rule = bazel_sharding.BazelRule("mock", "small")
bazel_sharding.add_rule_to_best_shard(rule, shards, optimum)
assert shards[3][0] == old_rule_medium
assert shards[3][-1] == rule
assert all(shard[-1] == old_rule for shard in shards[0:3])
| [CI] Make bazel sharding for parallel buildkite more intelligent (#29221)
This PR implements two changes to our `bazel-sharding.py` script, used for determining which bazel tests to run on each instance when buildkite parallelism is used:
* An ability to filter tests before they are sharded, using the same logic as `bazel test`. This is done by specifying the `--tag_filters` argument, eg. `--tag_filters=air,-gpu`. If we filter tests with `bazel test` *after* they are sharded, we can end up with imbalanced shards as eg. all tests we want to filter out are assigned to one shard. This feature is enabled for Serve tests and it will be required for the changes I want to make to AIR CI.
* A new algorithm to balance the shards, finally implementing what that comment was asking for all this time. Instead of trying to assign the same number of tests (which have variable timeouts) to each shard, the new algorithm tries to make sure each shard will finish in more or less the same time. This is achieved through a simple but good enough heuristic. The old algorithm can still be accessed through the `--sharding_strategy` argument.
Those two changes do cause the complexity of the script to increase, necessitating proper testing. In order to facilitate that, this PR also adds a basic buildkite test harness for CI tools/scripts.
After this PR is merged, the next step will be to move most of our manually parallelized jobs to use buildkite parallelism with the new logic here.
Signed-off-by: Antoni Baum <antoni.baum@protonmail.com> | test_add_rule_to_best_shard | d1aa5608979891e3dd859c07fa919fa01cfead5f | ray | test_bazel_sharding.py | 10 | 25 | https://github.com/ray-project/ray.git | 7 | 291 | 0 | 61 | 460 | Python | {
"docstring": "Test that the best shard in optimal strategy is chosen correctly.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | def test_add_rule_to_best_shard():
# If we start with an empty list, then add to first shard
shards: List[List[bazel_sharding.BazelRule]] = [list() for _ in range(4)]
optimum = 600
rule = bazel_sharding.BazelRule("mock", "medium")
bazel_sharding.add_rule_to_best_shard(rule, shards, optimum)
assert shards[0][0] == rule
assert all(not shard for shard in shards[1:])
# Add to first shard below optimum
old_rule = bazel_sharding.BazelRule("mock", "medium")
shards: List[List[bazel_sharding.BazelRule]] = [[old_rule] for _ in range(4)]
shards[3] = []
optimum = old_rule.actual_timeout_s
rule = bazel_sharding.BazelRule("mock", "small")
bazel_sharding.add_rule_to_best_shard(rule, shards, optimum)
assert shards[3][0] == rule
assert all(shard[-1] == old_rule for shard in shards[0:3])
# If all shards are above or equal optimum, add to the one with the smallest
# difference
old_rule = bazel_sharding.BazelRule("mock", "large")
shards: List[List[bazel_sharding.BazelRule]] = [[old_rule] for _ in range(4)]
optimum = old_rule.actual_timeout_s
old_rule_medium = bazel_sharding.BazelRule("mock", "medium")
shards[3][0] = old_rule_medium
rule = bazel_sharding.BazelRule("mock", "small")
bazel_sharding.add_rule_to_best_shard(rule, shards, optimum)
assert shards[3][0] == old_rule_medium
assert shards[3][-1] == rule
assert all(shard[-1] == old_rule for shard in shards[0:3])
|
|
110,798 | 312,146 | 37 | homeassistant/components/isy994/binary_sensor.py | 9 | 5 | def async_heartbeat(self) -> None:
self._computed_state = False
self._restart_timer()
self.async_write_ha_stat | Enable strict typing for isy994 (#65439)
Co-authored-by: Martin Hjelmare <marhje52@gmail.com> | async_heartbeat | 6c38a6b5697bcf4587e00101771001bf596974f9 | core | binary_sensor.py | 7 | 11 | https://github.com/home-assistant/core.git | 1 | 23 | 0 | 9 | 42 | Python | {
"docstring": "Mark the device as online, and restart the 25 hour timer.\n\n This gets called when the heartbeat node beats, but also when the\n parent sensor sends any events, as we can trust that to mean the device\n is online. This mitigates the risk of false positives due to a single\n missed heartbeat event.\n ",
"language": "en",
"n_whitespaces": 88,
"n_words": 53,
"vocab_size": 42
} | def async_heartbeat(self) -> None:
self._computed_state = False
self._restart_timer()
self.async_write_ha_state()
|
|
39,861 | 166,848 | 49 | pandas/tests/util/test_assert_series_equal.py | 24 | 17 | def test_assert_series_equal_interval_dtype_mismatch():
# https://github.com/pandas-dev/pandas/issues/32747
left = Series([pd.Interval(0, 1, "right")], dtype="interval")
right = left.astype(object)
msg =
tm.assert_series_equal(left, right, check_dtype=False)
with pytest.raises(AssertionError, match=msg):
tm.assert_series_equal(left, right, check_dtype=True)
| ENH: consistency of input args for boundaries - Interval (#46522) | test_assert_series_equal_interval_dtype_mismatch | 7e23a37e1c5bda81234801a6584563e2880769eb | pandas | test_assert_series_equal.py | 12 | 11 | https://github.com/pandas-dev/pandas.git | 1 | 72 | 0 | 20 | 123 | Python | {
"docstring": "Attributes of Series are different\n\nAttribute \"dtype\" are different\n\\\\[left\\\\]: interval\\\\[int64, right\\\\]\n\\\\[right\\\\]: object",
"language": "en",
"n_whitespaces": 11,
"n_words": 14,
"vocab_size": 12
} | def test_assert_series_equal_interval_dtype_mismatch():
# https://github.com/pandas-dev/pandas/issues/32747
left = Series([pd.Interval(0, 1, "right")], dtype="interval")
right = left.astype(object)
msg =
tm.assert_series_equal(left, right, check_dtype=False)
with pytest.raises(AssertionError, match=msg):
tm.assert_series_equal(left, right, check_dtype=True)
|
|
23,106 | 108,225 | 85 | lib/matplotlib/__init__.py | 35 | 10 | def rc_file_defaults():
# | Fix removed cross-references | rc_file_defaults | 7c6c5f6215b40a27cfefb7bf21246299fd9b3a1e | matplotlib | __init__.py | 12 | 5 | https://github.com/matplotlib/matplotlib.git | 3 | 41 | 0 | 32 | 72 | Python | {
"docstring": "\n Restore the `.rcParams` from the original rc file loaded by Matplotlib.\n\n Style-blacklisted `.rcParams` (defined in\n ``matplotlib.style.core.STYLE_BLACKLIST``) are not updated.\n ",
"language": "en",
"n_whitespaces": 32,
"n_words": 19,
"vocab_size": 17
} | def rc_file_defaults():
# Deprecation warnings were already handled when creating rcParamsOrig, no
# need to reemit them here.
with _api.suppress_matplotlib_deprecation_warning():
from .style.core import STYLE_BLACKLIST
rcParams.update({k: rcParamsOrig[k] for k in rcParamsOrig
if k not in STYLE_BLACKLIST})
|
|
3,587 | 20,845 | 153 | pipenv/patched/notpip/_vendor/rich/syntax.py | 21 | 12 | def lexer(self) -> Optional[Lexer]:
if isinstance(self._lexer, Lexer):
return self._lexer
try:
return get_lexer_by_name(
self._lexer,
stripnl=False,
ensurenl=True,
tabsize=self.tab_size,
)
except ClassNotFound:
| check point progress on only bringing in pip==22.0.4 (#4966)
* vendor in pip==22.0.4
* updating vendor packaging version
* update pipdeptree to fix pipenv graph with new version of pip.
* Vendoring of pip-shims 0.7.0
* Vendoring of requirementslib 1.6.3
* Update pip index safety restrictions patch for pip==22.0.4
* Update patches
* exclude pyptoject.toml from black to see if that helps.
* Move this part of the hash collection back to the top (like prior implementation) because it affects the outcome of this test now in pip 22.0.4 | lexer | f3166e673fe8d40277b804d35d77dcdb760fc3b3 | pipenv | syntax.py | 11 | 16 | https://github.com/pypa/pipenv.git | 3 | 54 | 0 | 19 | 83 | Python | {
"docstring": "The lexer for this syntax, or None if no lexer was found.\n\n Tries to find the lexer by name if a string was passed to the constructor.\n ",
"language": "en",
"n_whitespaces": 41,
"n_words": 27,
"vocab_size": 21
} | def lexer(self) -> Optional[Lexer]:
if isinstance(self._lexer, Lexer):
return self._lexer
try:
return get_lexer_by_name(
self._lexer,
stripnl=False,
ensurenl=True,
tabsize=self.tab_size,
)
except ClassNotFound:
return None
|
|
54,182 | 215,808 | 19 | tests/pytests/functional/modules/file/test_replace.py | 10 | 5 | def test_numeric_repl(file, multiline_file):
file.replace(multiline_fi | Add some funtional tests
Add functional tests for the following:
- file.readlink
- file.replace
- file.symlink
Remove unit tests for file.replace as they are duplicated in the added
functional test | test_numeric_repl | a35b29b2651bf33c5d5b45e64bc7765ffde4aff4 | salt | test_replace.py | 8 | 3 | https://github.com/saltstack/salt.git | 1 | 27 | 0 | 10 | 46 | Python | {
"docstring": "\n This test covers cases where the replacement string is numeric. The CLI\n parser yaml-fies it into a numeric type. If not converted back to a string\n type in file.replace, a TypeError occurs when the replace is attempted. See\n https://github.com/saltstack/salt/issues/9097 for more information.\n ",
"language": "en",
"n_whitespaces": 58,
"n_words": 42,
"vocab_size": 37
} | def test_numeric_repl(file, multiline_file):
file.replace(multiline_file, r"Etiam", 123)
assert "123" in multiline_file.read_text()
|
|
43,005 | 179,715 | 29 | gradio/components.py | 8 | 4 | def set_interpret_parameters(self, segments=16):
self.interpretation_segments = segments
retu | Blocks-Components
- fixes
- format | set_interpret_parameters | 7fa8e45b6782d545fa0ead112d92d13bdad7417c | gradio | components.py | 7 | 3 | https://github.com/gradio-app/gradio.git | 1 | 17 | 0 | 8 | 29 | Python | {
"docstring": "\n Calculates interpretation score of image subsections by splitting the image into subsections, then using a \"leave one out\" method to calculate the score of each subsection by whiting out the subsection and measuring the delta of the output value.\n Parameters:\n segments (int): Number of interpretation segments to split image into.\n ",
"language": "en",
"n_whitespaces": 79,
"n_words": 50,
"vocab_size": 35
} | def set_interpret_parameters(self, segments=16):
self.interpretation_segments = segments
return self
|
|
72,207 | 248,309 | 24 | synapse/storage/engines/sqlite.py | 10 | 5 | def can_native_upsert(self) -> bool:
return sqlite3.sqlite_version_info >= (3, 2 | Tidy up and type-hint the database engine modules (#12734)
Co-authored-by: Sean Quah <8349537+squahtx@users.noreply.github.com> | can_native_upsert | 1fe202a1a3343fad77da270ffe0923a46f1944dd | synapse | sqlite.py | 7 | 6 | https://github.com/matrix-org/synapse.git | 1 | 20 | 0 | 10 | 32 | Python | {
"docstring": "\n Do we support native UPSERTs? This requires SQLite3 3.24+, plus some\n more work we haven't done yet to tell what was inserted vs updated.\n ",
"language": "en",
"n_whitespaces": 46,
"n_words": 24,
"vocab_size": 23
} | def can_native_upsert(self) -> bool:
return sqlite3.sqlite_version_info >= (3, 24, 0)
|
|
31,405 | 138,397 | 97 | dashboard/state_aggregator.py | 29 | 16 | async def get_actors(self) -> dict:
reply = await self._client.get_all_actor_info(timeout=DEFAULT_RPC_TIMEOUT)
result = {}
for message in rep | [State Observability] Tasks and Objects API (#23912)
This PR implements ray list tasks and ray list objects APIs.
NOTE: You can ignore the merge conflict for now. It is because the first PR was reverted. There's a fix PR open now. | get_actors | 30ab5458a7e4ba2351d5e1beef8c8797b5946493 | ray | state_aggregator.py | 13 | 14 | https://github.com/ray-project/ray.git | 2 | 67 | 0 | 22 | 111 | Python | {
"docstring": "List all actor information from the cluster.\n\n Returns:\n {actor_id -> actor_data_in_dict}\n actor_data_in_dict's schema is in ActorState\n ",
"language": "en",
"n_whitespaces": 52,
"n_words": 16,
"vocab_size": 16
} | async def get_actors(self) -> dict:
reply = await self._client.get_all_actor_info(timeout=DEFAULT_RPC_TIMEOUT)
result = {}
for message in reply.actor_table_data:
data = self._message_to_dict(message=message, fields_to_decode=["actor_id"])
data = filter_fields(data, ActorState)
result[data["actor_id"]] = data
return result
|
|
25,051 | 113,876 | 833 | mindsdb/api/mysql/mysql_proxy/mysql_proxy.py | 181 | 42 | def insert_predictor_answer(self, insert):
model_interface = self.session.model_interface
data_store = self.session.data_store
select_data_query = insert.get('select_data_query')
if isinstance(select_data_query, str) is False or len(select_data_query) == 0:
self.packet(
ErrPacket,
err_code=ERR.ER_WRONG_ARGUMENTS,
msg="'select_data_query' should not be empty"
).send()
return
models = model_interface.get_models()
if insert['name'] in [x['name'] for x in models]:
self.packet(
ErrPacket,
err_code=ERR.ER_WRONG_ARGUMENTS,
msg=f"predictor with name '{insert['name']}'' already exists"
).send()
return
kwargs = {}
if isinstance(insert.get('training_options'), str) \
and len(insert['training_options']) > 0:
try:
kwargs = json.loads(insert['training_options'])
except Exception:
self.packet(
ErrPacket,
err_code=ERR.ER_WRONG_ARGUMENTS,
msg='training_options should be in valid JSON string'
).send()
return
integration = self.session.integration
if isinstance(integration, str) is False or len(integration) == 0:
self.packet(
ErrPacket,
err_code=E | fix | insert_predictor_answer | 551205a18ac2ac19626f4e4ffb2ed88fcad705b9 | mindsdb | mysql_proxy.py | 16 | 63 | https://github.com/mindsdb/mindsdb.git | 18 | 445 | 0 | 109 | 713 | Python | {
"docstring": " Start learn new predictor.\n Parameters:\n - insert - dict with keys as columns of mindsb.predictors table.\n ",
"language": "en",
"n_whitespaces": 47,
"n_words": 16,
"vocab_size": 15
} | def insert_predictor_answer(self, insert):
model_interface = self.session.model_interface
data_store = self.session.data_store
select_data_query = insert.get('select_data_query')
if isinstance(select_data_query, str) is False or len(select_data_query) == 0:
self.packet(
ErrPacket,
err_code=ERR.ER_WRONG_ARGUMENTS,
msg="'select_data_query' should not be empty"
).send()
return
models = model_interface.get_models()
if insert['name'] in [x['name'] for x in models]:
self.packet(
ErrPacket,
err_code=ERR.ER_WRONG_ARGUMENTS,
msg=f"predictor with name '{insert['name']}'' already exists"
).send()
return
kwargs = {}
if isinstance(insert.get('training_options'), str) \
and len(insert['training_options']) > 0:
try:
kwargs = json.loads(insert['training_options'])
except Exception:
self.packet(
ErrPacket,
err_code=ERR.ER_WRONG_ARGUMENTS,
msg='training_options should be in valid JSON string'
).send()
return
integration = self.session.integration
if isinstance(integration, str) is False or len(integration) == 0:
self.packet(
ErrPacket,
err_code=ERR.ER_WRONG_ARGUMENTS,
msg='select_data_query can be used only in query from database'
).send()
return
insert['select_data_query'] = insert['select_data_query'].replace(r"\'", "'")
ds_name = data_store.get_vacant_name(insert['name'])
ds = data_store.save_datasource(ds_name, integration, {'query': insert['select_data_query']})
insert['predict'] = [x.strip() for x in insert['predict'].split(',')]
ds_data = data_store.get_datasource(ds_name)
if ds_data is None:
raise Exception(f"DataSource '{ds_name}' does not exists")
ds_columns = [x['name'] for x in ds_data['columns']]
for col in insert['predict']:
if col not in ds_columns:
data_store.delete_datasource(ds_name)
raise Exception(f"Column '{col}' not exists")
try:
insert['predict'] = self._check_predict_columns(insert['predict'], ds_columns)
except Exception:
data_store.delete_datasource(ds_name)
raise
model_interface.learn(
insert['name'], ds, insert['predict'], ds_data['id'], kwargs=kwargs, delete_ds_on_fail=True
)
self.packet(OkPacket).send()
|
|
85,115 | 285,032 | 469 | openbb_terminal/portfolio/portfolio_model.py | 78 | 23 | def populate_historical_trade_data(self):
trade_data = self.__orderbook.pivot(
index="Date",
columns="Ticker",
values=[
"Type",
"Sector",
"Industry",
"Country",
"Price",
"Quantity",
| Overhaul Portfolio class (#2021)
* adds pythonic portfolio class
* start calculate trades refactoring
* adds comments to portfolio model - delete afterwards
* finish calculate trades refactoring
* restore original portfolio_model.py
* implement calculate_allocations
* adapt and test controller load, show, bench, alloc and perf
* add old code that was ok
* adapt controller
* adapt portfolio_view
* run black on pythonic_portfolio.py
* fix crypto bug
* change column name in example datasets
* substitute portfolio_model.py
* update show command
* push cumulative returns calculation to model
* fix last change in cumulative returns
* add comments on possibly unused code
* run black on changes
* bring metrics from helper to model
* push rolling metrics from view to model
* Details and linting
* Fix tests
* remove empty attribute and rename class
* fix view and controller rf
* change returns calculation method
* remove CASH from code
* remove cash from tickers_list
* run black on changes
* change function name
* adapt to PortfolioModel
* fix tests
* fix tests on help
* fix linting
* call metrics from PortfolioModel
* call drawdown from model
* fix some mypy issues
* fix remaining mypy issues
* fix test
* Fix linting
* Remove unused function
* Small fixes
* Remove old code and adjust summary to simply work
* Update the Excel since CASH is no longer a thing
* Fix tests
* Update the csvs
* Updates to usage of full_shares and more details
* Fix -t flag for perf
Co-authored-by: Jeroen Bouma <jer.bouma@gmail.com> | populate_historical_trade_data | 2c3e10a128fa0ce4e937d8d50dc0cd6d7cd11485 | OpenBBTerminal | portfolio_model.py | 12 | 34 | https://github.com/OpenBB-finance/OpenBBTerminal.git | 1 | 164 | 0 | 65 | 282 | Python | {
"docstring": "Create a new dataframe to store historical prices by ticker",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | def populate_historical_trade_data(self):
trade_data = self.__orderbook.pivot(
index="Date",
columns="Ticker",
values=[
"Type",
"Sector",
"Industry",
"Country",
"Price",
"Quantity",
"Fees",
"Premium",
"Investment",
"Side",
"Currency",
],
)
# Make historical prices columns a multi-index. This helps the merging.
self.portfolio_historical_prices.columns = pd.MultiIndex.from_product(
[["Close"], self.portfolio_historical_prices.columns]
)
# Merge with historical close prices (and fillna)
trade_data = pd.merge(
trade_data,
self.portfolio_historical_prices,
how="right",
left_index=True,
right_index=True,
).fillna(0)
# Accumulate quantity held by trade date
trade_data["Quantity"] = trade_data["Quantity"].cumsum()
trade_data["Investment"] = trade_data["Investment"].cumsum()
trade_data.loc[:, ("Investment", "Total")] = trade_data["Investment"][
self.tickers_list
].sum(axis=1)
self.historical_trade_data = trade_data
|
|
@fails_if_pypy
@pytest.mark.parametrize("parser", ["liac-arff", "pandas"]) | 75,979 | 259,898 | 129 | sklearn/datasets/tests/test_openml.py | 47 | 20 | def test_fetch_openml_equivalence_array_dataframe(monkeypatch, parser):
pytest.importorskip("pandas")
data_id = 61
_monkey_patch_webbased_functions(monkeypatch, data_id, gzip_response=True)
bunch_as_frame_true = fetch_openml(
data_id=data_id,
as_frame=True,
cache=False,
parser=parser,
)
bunch_as_frame_false = fetch_openml(
data_id=data_id,
as_frame=False,
cache=False,
parser=parser,
)
assert_allclose(bunch_as_frame_false.data, bunch_as_frame_true.data)
assert_array_equal(bunch_as_frame_false.target, bunch_as_frame_true.target)
# Known failure of PyPy for OpenML. See the following issue:
# https://github.com/scikit-learn/scikit-learn/issues/18906
@fails_if_pypy
@pytest.mark.parametrize("parser", ["liac-arff", "pandas"]) | ENH improve ARFF parser using pandas (#21938)
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
Co-authored-by: Olivier Grisel <olivier.grisel@gmail.com>
Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com> | test_fetch_openml_equivalence_array_dataframe | a47d569e670fd4102af37c3165c9b1ddf6fd3005 | scikit-learn | test_openml.py | 9 | 18 | https://github.com/scikit-learn/scikit-learn.git | 1 | 89 | 1 | 39 | 167 | Python | {
"docstring": "Check the equivalence of the dataset when using `as_frame=False` and\n `as_frame=True`.\n ",
"language": "en",
"n_whitespaces": 17,
"n_words": 11,
"vocab_size": 10
} | def test_fetch_openml_equivalence_array_dataframe(monkeypatch, parser):
pytest.importorskip("pandas")
data_id = 61
_monkey_patch_webbased_functions(monkeypatch, data_id, gzip_response=True)
bunch_as_frame_true = fetch_openml(
data_id=data_id,
as_frame=True,
cache=False,
parser=parser,
)
bunch_as_frame_false = fetch_openml(
data_id=data_id,
as_frame=False,
cache=False,
parser=parser,
)
assert_allclose(bunch_as_frame_false.data, bunch_as_frame_true.data)
assert_array_equal(bunch_as_frame_false.target, bunch_as_frame_true.target)
# Known failure of PyPy for OpenML. See the following issue:
# https://github.com/scikit-learn/scikit-learn/issues/18906
@fails_if_pypy
@pytest.mark.parametrize("parser", ["liac-arff", "pandas"]) |
73,944 | 252,396 | 76 | mitmproxy/contrib/kaitaistruct/google_protobuf.py | 17 | 12 | def wire_type(self):
if hasattr(self, '_m_wire_type'):
return self._m_wire_type
self._m_wire_type = Kaita | update kaitai definitions | wire_type | 002f919dda5f01d067c2e786426c68751551d15c | mitmproxy | google_protobuf.py | 12 | 5 | https://github.com/mitmproxy/mitmproxy.git | 2 | 51 | 0 | 15 | 83 | Python | {
"docstring": "\"Wire type\" is a part of the \"key\" that carries enough\n information to parse value from the wire, i.e. read correct\n amount of bytes, but there's not enough informaton to\n interprete in unambiguously. For example, one can't clearly\n distinguish 64-bit fixed-sized integers from 64-bit floats,\n signed zigzag-encoded varints from regular unsigned varints,\n arbitrary bytes from UTF-8 encoded strings, etc.\n ",
"language": "en",
"n_whitespaces": 136,
"n_words": 59,
"vocab_size": 51
} | def wire_type(self):
if hasattr(self, '_m_wire_type'):
return self._m_wire_type
self._m_wire_type = KaitaiStream.resolve_enum(GoogleProtobuf.Pair.WireTypes, (self.key.value & 7))
return getattr(self, '_m_wire_type', None)
|
|
73,687 | 251,333 | 80 | mitmproxy/connection.py | 18 | 7 | def address(self): # pragma: no cover
warnings.warn(
"Client.address is deprecated, use Client.peername instead.",
D | make it black! | address | b3587b52b25077f68116b9852b041d33e7fc6601 | mitmproxy | connection.py | 8 | 7 | https://github.com/mitmproxy/mitmproxy.git | 1 | 23 | 0 | 18 | 40 | Python | {
"docstring": "*Deprecated:* An outdated alias for Client.peername.",
"language": "en",
"n_whitespaces": 5,
"n_words": 6,
"vocab_size": 6
} | def address(self): # pragma: no cover
warnings.warn(
"Client.address is deprecated, use Client.peername instead.",
DeprecationWarning,
stacklevel=2,
)
return self.peername
|
|
f"""\
To get asquare Jordan block matrix use a morebanded matrix | 48,242 | 196,907 | 80 | sympy/matrices/common.py | 28 | 21 | def jordan_block(kls, size=None, eigenvalue=None, *, band='upper', **kwargs):
if 'r | Update the Matrix.jordan_block() rows and cols kwargs deprecation | jordan_block | a4fdabab38def4bf6b4007f8cd67d6944740b303 | sympy | common.py | 12 | 45 | https://github.com/sympy/sympy.git | 16 | 239 | 3 | 19 | 109 | Python | {
"docstring": "Returns a Jordan block\n\n Parameters\n ==========\n\n size : Integer, optional\n Specifies the shape of the Jordan block matrix.\n\n eigenvalue : Number or Symbol\n Specifies the value for the main diagonal of the matrix.\n\n .. note::\n The keyword ``eigenval`` is also specified as an alias\n of this keyword, but it is not recommended to use.\n\n We may deprecate the alias in later release.\n\n band : 'upper' or 'lower', optional\n Specifies the position of the off-diagonal to put `1` s on.\n\n cls : Matrix, optional\n Specifies the matrix class of the output form.\n\n If it is not specified, the class type where the method is\n being executed on will be returned.\n\n rows, cols : Integer, optional\n Specifies the shape of the Jordan block matrix. See Notes\n section for the details of how these key works.\n\n .. deprecated:: 1.4\n The rows and cols parameters are deprecated and will be\n removed in a future version.\n\n\n Returns\n =======\n\n Matrix\n A Jordan block matrix.\n\n Raises\n ======\n\n ValueError\n If insufficient arguments are given for matrix size\n specification, or no eigenvalue is given.\n\n Examples\n ========\n\n Creating a default Jordan block:\n\n >>> from sympy import Matrix\n >>> from sympy.abc import x\n >>> Matrix.jordan_block(4, x)\n Matrix([\n [x, 1, 0, 0],\n [0, x, 1, 0],\n [0, 0, x, 1],\n [0, 0, 0, x]])\n\n Creating an alternative Jordan block matrix where `1` is on\n lower off-diagonal:\n\n >>> Matrix.jordan_block(4, x, band='lower')\n Matrix([\n [x, 0, 0, 0],\n [1, x, 0, 0],\n [0, 1, x, 0],\n [0, 0, 1, x]])\n\n Creating a Jordan block with keyword arguments\n\n >>> Matrix.jordan_block(size=4, eigenvalue=x)\n Matrix([\n [x, 1, 0, 0],\n [0, x, 1, 0],\n [0, 0, x, 1],\n [0, 0, 0, x]])\n\n Notes\n =====\n\n .. deprecated:: 1.4\n This feature is deprecated and will be removed in a future\n version.\n\n The keyword arguments ``size``, ``rows``, ``cols`` relates to\n the Jordan block size specifications.\n\n If you want to create a square Jordan block, specify either\n one of the three arguments.\n\n If you want to create a rectangular Jordan block, specify\n ``rows`` and ``cols`` individually.\n\n +--------------------------------+---------------------+\n | Arguments Given | Matrix Shape |\n +----------+----------+----------+----------+----------+\n | size | rows | cols | rows | cols |\n +==========+==========+==========+==========+==========+\n | size | Any | size | size |\n +----------+----------+----------+----------+----------+\n | | None | ValueError |\n | +----------+----------+----------+----------+\n | None | rows | None | rows | rows |\n | +----------+----------+----------+----------+\n | | None | cols | cols | cols |\n + +----------+----------+----------+----------+\n | | rows | cols | rows | cols |\n +----------+----------+----------+----------+----------+\n\n References\n ==========\n\n .. [1] https://en.wikipedia.org/wiki/Jordan_matrix\n \n The 'rows' and 'cols' keywords to Matrix.jordan_block() are\n deprecated. Use the 'size' parameter instead.\n \\\n To get a non-square Jordan block matrix use a more generic\n banded matrix constructor, like\n",
"language": "en",
"n_whitespaces": 1426,
"n_words": 442,
"vocab_size": 190
} | def jordan_block(kls, size=None, eigenvalue=None, *, band='upper', **kwargs):
if 'rows' in kwargs or 'cols' in kwargs:
msg =
if 'rows' in kwargs and 'cols' in kwargs:
msg += f |
75,236 | 258,441 | 71 | rest_api/rest_api/utils.py | 17 | 11 | def get_openapi_specs() -> dict:
app = get_app() | bug: fix the docs rest api reference url (#3775)
* bug: fix the docs rest api reference url
* revert openapi json changes
* remove last line on json files
* Add explanation about `servers` and remove `servers` parameter from FastAPI
* generate openapi schema without empty end line | get_openapi_specs | 86ade4817eda3142d2ddef65a0b1e29ffee770e3 | haystack | utils.py | 12 | 17 | https://github.com/deepset-ai/haystack.git | 1 | 56 | 0 | 17 | 89 | Python | {
"docstring": "\n Used to autogenerate OpenAPI specs file to use in the documentation.\n\n Returns `servers` to specify base URL for OpenAPI Playground (see https://swagger.io/docs/specification/api-host-and-base-path/)\n\n See `.github/utils/generate_openapi_specs.py`\n ",
"language": "en",
"n_whitespaces": 37,
"n_words": 24,
"vocab_size": 21
} | def get_openapi_specs() -> dict:
app = get_app()
return get_openapi(
title=app.title,
version=app.version,
openapi_version=app.openapi_version,
description=app.description,
routes=app.routes,
servers=[{"url": "http://localhost:8000"}],
)
|
|
14,099 | 66,068 | 32 | erpnext/hr/doctype/employee/employee.py | 47 | 16 | def get_all_employee_emails(company):
employee_list = frappe.get_all(
"Employee", fields=["name", "employee_name"], filters={"status": "Active", "company": company}
)
employee_emails = []
for | style: format code with black | get_all_employee_emails | 494bd9ef78313436f0424b918f200dab8fc7c20b | erpnext | employee.py | 12 | 15 | https://github.com/frappe/erpnext.git | 6 | 90 | 0 | 38 | 156 | Python | {
"docstring": "Returns list of employee emails either based on user_id or company_email",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | def get_all_employee_emails(company):
employee_list = frappe.get_all(
"Employee", fields=["name", "employee_name"], filters={"status": "Active", "company": company}
)
employee_emails = []
for employee in employee_list:
if not employee:
continue
user, company_email, personal_email = frappe.db.get_value(
"Employee", employee, ["user_id", "company_email", "personal_email"]
)
email = user or company_email or personal_email
if email:
employee_emails.append(email)
return employee_emails
|
|
18,657 | 90,257 | 368 | tests/snuba/api/endpoints/test_organization_group_index.py | 81 | 43 | def test_in_non_semver_projects_resolved_in_next_release_is_equated_to_in_release(self):
release_1 = self.create_release(
date_added=timezon | ref(tests): Remove `get_valid_response()` (#34822) | test_in_non_semver_projects_resolved_in_next_release_is_equated_to_in_release | 096b5511e244eecd8799b2a0324655207ce8985e | sentry | test_organization_group_index.py | 17 | 33 | https://github.com/getsentry/sentry.git | 1 | 249 | 0 | 59 | 407 | Python | {
"docstring": "\n Test that ensures that if we basically know the next release when clicking on Resolved\n In Next Release because that release exists, then we can short circuit setting\n GroupResolution to type \"inNextRelease\", and then having `clear_exrired_resolutions` run\n once a new release is created to convert GroupResolution to in_release and set Activity.\n Basically we treat \"ResolvedInNextRelease\" as \"ResolvedInRelease\" when there is a release\n that was created after the last release associated with the group being resolved\n ",
"language": "en",
"n_whitespaces": 125,
"n_words": 75,
"vocab_size": 55
} | def test_in_non_semver_projects_resolved_in_next_release_is_equated_to_in_release(self):
release_1 = self.create_release(
date_added=timezone.now() - timedelta(minutes=45), version="foobar 1"
)
release_2 = self.create_release(version="foobar 2")
self.create_release(version="foobar 3")
group = self.store_event(
data={
"timestamp": iso_format(before_now(seconds=12)),
"fingerprint": ["group-1"],
"release": release_1.version,
},
project_id=self.project.id,
).group
self.login_as(user=self.user)
response = self.get_success_response(
qs_params={"id": group.id}, status="resolvedInNextRelease"
)
assert response.data["status"] == "resolved"
assert response.data["statusDetails"]["inNextRelease"]
grp_resolution = GroupResolution.objects.filter(group=group)
assert len(grp_resolution) == 1
grp_resolution = grp_resolution[0]
assert grp_resolution.current_release_version == release_1.version
assert grp_resolution.release.id == release_2.id
assert grp_resolution.type == GroupResolution.Type.in_release
assert grp_resolution.status == GroupResolution.Status.resolved
activity = Activity.objects.filter(
group=grp_resolution.group,
type=Activity.SET_RESOLVED_IN_RELEASE,
ident=grp_resolution.id,
).first()
assert activity.data["version"] == release_2.version
|
|
@pytest.fixture | 4,995 | 26,436 | 21 | saleor/plugins/webhook/tests/subscription_webhooks/fixtures.py | 10 | 8 | def subscription_order_updated_webhook(subscription_webhook):
return subscription_webhook(
ORDER_UPDATED_SUBSCRIPTION_QUERY, Webhook | Add Webhook payload via graphql subscriptions (#9394)
* Add PoC of webhook subscriptions
* add async webhooks subscription payloads feature
* remove unneeded file
* add translations subscription handling, fixes after review
* remove todo
* add descriptions
* add descriptions, move subsrciption_payloads.py
* refactor
* fix imports, add changelog
* check_document_is_single_subscription refactor
Co-authored-by: Maciej Korycinski <maciej@mirumee.com>
Co-authored-by: Marcin Gębala <5421321+maarcingebala@users.noreply.github.com> | subscription_order_updated_webhook | aca6418d6c36956bc1ab530e6ef7e146ec9df90c | saleor | fixtures.py | 8 | 4 | https://github.com/saleor/saleor.git | 1 | 14 | 1 | 10 | 36 | Python | {
"docstring": "\n subscription{\n event{\n ...on OrderConfirmed{\n order{\n id\n }\n }\n }\n }\n",
"language": "en",
"n_whitespaces": 69,
"n_words": 10,
"vocab_size": 7
} | def subscription_order_updated_webhook(subscription_webhook):
return subscription_webhook(
ORDER_UPDATED_SUBSCRIPTION_QUERY, WebhookEventAsyncType.ORDER_UPDATED
)
ORDER_CONFIRMED_SUBSCRIPTION_QUERY =
@pytest.fixture |
35,391 | 153,357 | 1,180 | modin/experimental/core/execution/native/implementations/omnisci_on_native/omnisci_worker.py | 295 | 55 | def cast_to_compatible_types(table):
schema = table.schema
new_schema = schema
need_cast = False
uint_to_int_cast = False
new_cols = {}
uint_to_int_map = {
pa.uint8(): pa.int16(),
pa.uint16(): pa.int32(),
pa.uint32(): pa.int64(),
pa.uint64(): pa.int64(), # May cause overflow
}
for i, field in enumerate(schema):
# Currently OmniSci doesn't support Arrow table import with
# dictionary columns. Here we cast dictionaries until support
# is in place.
# https://github.com/modin-project/modin/issues/1738
if pa.types.is_dictionary(field.type):
| FIX-#3368: support unsigned integers in OmniSci backend (#4256)
Signed-off-by: Dmitry Chigarev <dmitry.chigarev@intel.com>
Co-authored-by: Yaroslav Igoshev <Poolliver868@mail.ru> | cast_to_compatible_types | 241a46dd5f4dce7bc7f630b58c80d15222d6bde7 | modin | omnisci_worker.py | 16 | 56 | https://github.com/modin-project/modin.git | 11 | 382 | 0 | 171 | 613 | Python | {
"docstring": "\n Cast PyArrow table to be fully compatible with OmniSci.\n\n Parameters\n ----------\n table : pyarrow.Table\n Source table.\n\n Returns\n -------\n pyarrow.Table\n Table with fully compatible types with OmniSci.\n ",
"language": "en",
"n_whitespaces": 105,
"n_words": 26,
"vocab_size": 19
} | def cast_to_compatible_types(table):
schema = table.schema
new_schema = schema
need_cast = False
uint_to_int_cast = False
new_cols = {}
uint_to_int_map = {
pa.uint8(): pa.int16(),
pa.uint16(): pa.int32(),
pa.uint32(): pa.int64(),
pa.uint64(): pa.int64(), # May cause overflow
}
for i, field in enumerate(schema):
# Currently OmniSci doesn't support Arrow table import with
# dictionary columns. Here we cast dictionaries until support
# is in place.
# https://github.com/modin-project/modin/issues/1738
if pa.types.is_dictionary(field.type):
# Conversion for dictionary of null type to string is not supported
# in Arrow. Build new column for this case for now.
if pa.types.is_null(field.type.value_type):
mask = np.full(table.num_rows, True, dtype=bool)
new_col_data = np.empty(table.num_rows, dtype=str)
new_col = pa.array(new_col_data, pa.string(), mask)
new_cols[i] = new_col
else:
need_cast = True
new_field = pa.field(
field.name, pa.string(), field.nullable, field.metadata
)
new_schema = new_schema.set(i, new_field)
# OmniSci doesn't support importing Arrow's date type:
# https://github.com/omnisci/omniscidb/issues/678
elif pa.types.is_date(field.type):
# Arrow's date is the number of days since the UNIX-epoch, so we can convert it
# to a timestamp[s] (number of seconds since the UNIX-epoch) without losing precision
new_field = pa.field(
field.name, pa.timestamp("s"), field.nullable, field.metadata
)
new_schema = new_schema.set(i, new_field)
need_cast = True
# OmniSci doesn't support unsigned types
elif pa.types.is_unsigned_integer(field.type):
new_field = pa.field(
field.name,
uint_to_int_map[field.type],
field.nullable,
field.metadata,
)
new_schema = new_schema.set(i, new_field)
need_cast = True
uint_to_int_cast = True
# Such cast may affect the data, so we have to raise a warning about it
if uint_to_int_cast:
ErrorMessage.single_warning(
"OmniSci does not support unsigned integer types, such types will be rounded up to the signed equivalent."
)
for i, col in new_cols.items():
table = table.set_column(i, new_schema[i], col)
if need_cast:
try:
table = table.cast(new_schema)
except pa.lib.ArrowInvalid as e:
raise (OverflowError if uint_to_int_cast else RuntimeError)(
"An error occurred when trying to convert unsupported by OmniSci 'dtypes' "
+ f"to the supported ones, the schema to cast was: \n{new_schema}."
) from e
return table
|
|
1,807 | 9,959 | 20 | jina/types/request/data.py | 6 | 5 | def data(self) -> 'DataRequest._DataContent':
return DataRequest._DataCon | feat: star routing (#3900)
* feat(proto): adjust proto for star routing (#3844)
* feat(proto): adjust proto for star routing
* feat(proto): generate proto files
* feat(grpc): refactor grpclet interface (#3846)
* feat: refactor connection pool for star routing (#3872)
* feat(k8s): add more labels to k8s deployments
* feat(network): refactor connection pool
* feat(network): refactor k8s pool
* feat: star routing graph gateway (#3877)
* feat: star routing - refactor grpc data runtime (#3887)
* feat(runtimes): refactor grpc dataruntime
* fix(tests): adapt worker runtime tests
* fix(import): fix import
* feat(proto): enable sending multiple lists (#3891)
* feat: star routing gateway (#3893)
* feat: star routing gateway all protocols (#3897)
* test: add streaming and prefetch tests (#3901)
* feat(head): new head runtime for star routing (#3899)
* feat(head): new head runtime
* feat(head): new head runtime
* style: fix overload and cli autocomplete
* feat(network): improve proto comments
Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>
* feat(worker): merge docs in worker runtime (#3905)
* feat(worker): merge docs in worker runtime
* feat(tests): assert after clean up
* feat(tests): star routing runtime integration tests (#3908)
* fix(tests): fix integration tests
* test: test runtimes fast slow request (#3910)
* feat(zmq): purge zmq, zed, routing_table (#3915)
* feat(zmq): purge zmq, zed, routing_table
* style: fix overload and cli autocomplete
* feat(zmq): adapt comment in dependency list
* style: fix overload and cli autocomplete
* fix(tests): fix type tests
Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>
* test: add test gateway to worker connection (#3921)
* feat(pea): adapt peas for star routing (#3918)
* feat(pea): adapt peas for star routing
* style: fix overload and cli autocomplete
* feat(pea): add tests
* feat(tests): add failing head pea test
Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>
* feat(tests): integration tests for peas (#3923)
* feat(tests): integration tests for peas
* feat(pea): remove _inner_pea function
* feat: star routing container pea (#3922)
* test: rescue tests (#3942)
* fix: fix streaming tests (#3945)
* refactor: move docker run to run (#3948)
* feat: star routing pods (#3940)
* feat(pod): adapt pods for star routing
* feat(pods): adapt basepod to star routing
* feat(pod): merge pod and compound pod
* feat(tests): fix tests
* style: fix overload and cli autocomplete
* feat(test): add container pea int test
* feat(ci): remove more unnecessary tests
* fix(tests): remove jinad runtime
* feat(ci): remove latency tracking
* fix(ci): fix ci def
* fix(runtime): enable runtime to be exited
* fix(tests): wrap runtime test in process
* fix(runtimes): remove unused runtimes
* feat(runtimes): improve cancel wait
* fix(ci): build test pip again in ci
* fix(tests): fix a test
* fix(test): run async in its own process
* feat(pod): include shard in activate msg
* fix(pea): dont join
* feat(pod): more debug out
* feat(grpc): manage channels properly
* feat(pods): remove exitfifo
* feat(network): add simple send retry mechanism
* fix(network): await pool close
* fix(test): always close grpc server in worker
* fix(tests): remove container pea from tests
* fix(tests): reorder tests
* fix(ci): split tests
* fix(ci): allow alias setting
* fix(test): skip a test
* feat(pods): address comments
Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>
* test: unblock skipped test (#3957)
* feat: jinad pea (#3949)
* feat: jinad pea
* feat: jinad pea
* test: remote peas
* test: toplogy tests with jinad
* ci: parallel jobs
* feat(tests): add pod integration tests (#3958)
* feat(tests): add pod integration tests
* fix(tests): make tests less flaky
* fix(test): fix test
* test(pea): remote pea topologies (#3961)
* test(pea): remote pea simple topology
* test: remote pea topologies
* refactor: refactor streamer result handling (#3960)
* feat(k8s): adapt K8s Pod for StarRouting (#3964)
* test: optimize k8s test
* test: increase timeout and use different namespace
* test: optimize k8s test
* test: build and load image when needed
* test: refactor k8s test
* test: fix image name error
* test: fix k8s image load
* test: fix typoe port expose
* test: update tests in connection pool and handling
* test: remove unused fixture
* test: parameterize docker images
* test: parameterize docker images
* test: parameterize docker images
* feat(k8s): adapt k8s pod for star routing
* fix(k8s): dont overwrite add/remove function in pool
* fix(k8s): some fixes
* fix(k8s): some more fixes
* fix(k8s): linting
* fix(tests): fix tests
* fix(tests): fix k8s unit tests
* feat(k8s): complete k8s integration test
* feat(k8s): finish k8s tests
* feat(k8s): fix test
* fix(tests): fix test with no name
* feat(k8s): unify create/replace interface
* feat(k8s): extract k8s port constants
* fix(tests): fix tests
* fix(tests): wait for runtime being ready in tests
* feat(k8s): address comments
Co-authored-by: bwanglzu <bo.wang@jina.ai>
* feat(flow): adapt Flow for StarRouting (#3986)
* feat(flow): add routes
* feat(flow): adapt flow to star routing
* style: fix overload and cli autocomplete
* feat(flow): handle empty topologies
* feat(k8s): allow k8s pool disabling
* style: fix overload and cli autocomplete
* fix(test): fix test with mock
* fix(tests): fix more tests
* feat(flow): clean up tests
* style: fix overload and cli autocomplete
* fix(tests): fix more tests
* feat: add plot function (#3994)
* fix(tests): avoid hanging tests
* feat(flow): add type hinting
* fix(test): fix duplicate exec name in test
* fix(tests): fix more tests
* fix(tests): enable jinad test again
* fix(tests): random port fixture
* fix(style): replace quotes
Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>
Co-authored-by: Joan Fontanals <joan.martinez@jina.ai>
* feat(ci): bring back ci (#3997)
* feat(ci): enable ci again
* style: fix overload and cli autocomplete
* feat(ci): add latency tracking
* feat(ci): bring back some tests
* fix(tests): remove invalid port test
* feat(ci): disable daemon and distributed tests
* fix(tests): fix entrypoint in hub test
* fix(tests): wait for gateway to be ready
* fix(test): fix more tests
* feat(flow): do rolling update and scale sequentially
* fix(tests): fix more tests
* style: fix overload and cli autocomplete
* feat: star routing hanging pods (#4011)
* fix: try to handle hanging pods better
* test: hanging pods test work
* fix: fix topology graph problem
* test: add unit test to graph
* fix(tests): fix k8s tests
* fix(test): fix k8s test
* fix(test): fix k8s pool test
* fix(test): fix k8s test
* fix(test): fix k8s connection pool setting
* fix(tests): make runtime test more reliable
* fix(test): fix routes test
* fix(tests): make rolling update test less flaky
* feat(network): gurantee unique ports
* feat(network): do round robin for shards
* fix(ci): increase pytest timeout to 10 min
Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>
Co-authored-by: Joan Fontanals <joan.martinez@jina.ai>
* fix(ci): fix ci file
* feat(daemon): jinad pod for star routing
* Revert "feat(daemon): jinad pod for star routing"
This reverts commit ed9b37ac862af2e2e8d52df1ee51c0c331d76f92.
* feat(daemon): remote jinad pod support (#4042)
* feat(daemon): add pod tests for star routing
* feat(daemon): add remote pod test
* test(daemon): add remote pod arguments test
* test(daemon): add async scale test
* test(daemon): add rolling update test
* test(daemon): fix host
* feat(proto): remove message proto (#4051)
* feat(proto): remove message proto
* fix(tests): fix tests
* fix(tests): fix some more tests
* fix(tests): fix more tests
* fix(tests): fix more tests
* fix(tests): fix more tests
* fix(tests): fix more tests
* feat(proto): put docs back in data
* fix(proto): clean up
* feat(proto): clean up
* fix(tests): skip latency tracking
* fix(test): fix hub test
* fix(tests): fix k8s test
* fix(test): some test clean up
* fix(style): clean up style issues
* feat(proto): adjust for rebase
* fix(tests): bring back latency tracking
* fix(tests): fix merge accident
* feat(proto): skip request serialization (#4074)
* feat: add reduce to star routing (#4070)
* feat: add reduce on shards to head runtime
* test: add reduce integration tests with fixed order
* feat: add reduce on needs
* chore: get_docs_matrix_from_request becomes public
* style: fix overload and cli autocomplete
* docs: remove undeterministic results warning
* fix: fix uses_after
* test: assert correct num docs after reducing in test_external_pod
* test: correct asserts after reduce in test_rolling_update
* fix: no reduce if uses_after_address is set
* fix: get_docs_from_request only if needed
* fix: fix tests after merge
* refactor: move reduce from data_request_handler to head
* style: fix overload and cli autocomplete
* chore: apply suggestions
* fix: fix asserts
* chore: minor test fix
* chore: apply suggestions
* test: remove flow tests with external executor (pea)
* fix: fix test_expected_messages_routing
* fix: fix test_func_joiner
* test: adapt k8s test
Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>
* fix(k8s): fix static pool config
* fix: use custom protoc doc generator image (#4088)
* fix: use custom protoc doc generator image
* fix(docs): minor doc improvement
* fix(docs): use custom image
* fix(docs): copy docarray
* fix: doc building local only
* fix: timeout doc building
* fix: use updated args when building ContainerPea
* test: add container PeaFactory test
* fix: force pea close on windows (#4098)
* fix: dont reduce if uses exist (#4099)
* fix: dont use reduce if uses exist
* fix: adjust reduce tests
* fix: adjust more reduce tests
* fix: fix more tests
* fix: adjust more tests
* fix: ignore non jina resources (#4101)
* feat(executor): enable async executors (#4102)
* feat(daemon): daemon flow on star routing (#4096)
* test(daemon): add remote flow test
* feat(daemon): call scale in daemon
* feat(daemon): remove tail args and identity
* test(daemon): rename scalable executor
* test(daemon): add a small delay in async test
* feat(daemon): scale partial flow only
* feat(daemon): call scale directly in partial flow store
* test(daemon): use asyncio sleep
* feat(daemon): enable flow level distributed tests
* test(daemon): fix jinad env workspace config
* test(daemon): fix pod test use new port rolling update
* feat(daemon): enable distribuetd tests
* test(daemon): remove duplicate tests and zed runtime test
* test(daemon): fix stores unit test
* feat(daemon): enable part of distributed tests
* feat(daemon): enable part of distributed tests
* test: correct test paths
* test(daemon): add client test for remote flows
* test(daemon): send a request with jina client
* test(daemon): assert async generator
* test(daemon): small interval between tests
* test(daemon): add flow test for container runtime
* test(daemon): add flow test for container runtime
* test(daemon): fix executor name
* test(daemon): fix executor name
* test(daemon): use async client fetch result
* test(daemon): finish container flow test
* test(daemon): enable distributed in ci
* test(daemon): enable distributed in ci
* test(daemon): decare flows and pods
* test(daemon): debug ci if else
* test(daemon): debug ci if else
* test(daemon): decare flows and pods
* test(daemon): correct test paths
* test(daemon): add small delay for async tests
* fix: star routing fixes (#4100)
* docs: update docs
* fix: fix Request.__repr__
* docs: update flow remarks
* docs: fix typo
* test: add non_empty_fields test
* chore: remove non_empty_fields test
* feat: polling per endpoint (#4111)
* feat(polling): polling per endpoint configurable
* fix: adjust tests
* feat(polling): extend documentation
* style: fix overload and cli autocomplete
* fix: clean up
* fix: adjust more tests
* fix: remove repeat from flaky test
* fix: k8s test
* feat(polling): address pr feedback
* feat: improve docs
Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>
* feat(grpc): support connect grpc server via ssl tunnel (#4092)
* feat(grpc): support ssl grpc connect if port is 443
* fix(grpc): use https option instead of detect port automatically
* chore: fix typo
* fix: update jina/peapods/networking.py
Co-authored-by: Joan Fontanals <joan.martinez@jina.ai>
* fix: update jina/peapods/networking.py
Co-authored-by: Joan Fontanals <joan.martinez@jina.ai>
* fix: update jina/peapods/networking.py
Co-authored-by: Joan Fontanals <joan.martinez@jina.ai>
* test(networking): add test for peapods networking
* fix: address comments
Co-authored-by: Joan Fontanals <joan.martinez@jina.ai>
* feat(polling): unify polling args (#4113)
* fix: several issues for jinad pods (#4119)
* fix: activate for jinad pods
* fix: dont expose worker pod in partial daemon
* fix: workspace setting
* fix: containerized flows
* fix: hub test
* feat(daemon): remote peas on star routing (#4112)
* test(daemon): fix request in peas
* test(daemon): fix request in peas
* test(daemon): fix sync async client test
* test(daemon): enable remote peas test
* test(daemon): replace send message to send request
* test(daemon): declare pea tests in ci
* test(daemon): use pea args fixture
* test(daemon): head pea use default host
* test(daemon): fix peas topologies
* test(daemon): fix pseudo naming
* test(daemon): use default host as host
* test(daemon): fix executor path
* test(daemon): add remote worker back
* test(daemon): skip local remote remote topology
* fix: jinad pea test setup
* fix: jinad pea tests
* fix: remove invalid assertion
Co-authored-by: jacobowitz <tobias.jacobowitz@posteo.de>
* feat: enable daemon tests again (#4132)
* feat: enable daemon tests again
* fix: remove bogy empty script file
* fix: more jinad test fixes
* style: fix overload and cli autocomplete
* fix: scale and ru in jinad
* fix: fix more jinad tests
Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>
* fix: fix flow test
* fix: improve pea tests reliability (#4136)
Co-authored-by: Joan Fontanals <joan.martinez@jina.ai>
Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>
Co-authored-by: Deepankar Mahapatro <deepankar.mahapatro@jina.ai>
Co-authored-by: bwanglzu <bo.wang@jina.ai>
Co-authored-by: AlaeddineAbdessalem <alaeddine-13@live.fr>
Co-authored-by: Zhaofeng Miao <522856232@qq.com> | data | 933415bfa1f9eb89f935037014dfed816eb9815d | jina | data.py | 9 | 6 | https://github.com/jina-ai/jina.git | 1 | 19 | 0 | 6 | 35 | Python | {
"docstring": "Get the data contaned in this data request\n\n :return: the data content as an instance of _DataContent wrapping docs and groundtruths\n ",
"language": "en",
"n_whitespaces": 35,
"n_words": 21,
"vocab_size": 18
} | def data(self) -> 'DataRequest._DataContent':
return DataRequest._DataContent(self.proto.data)
|
|
81,498 | 275,883 | 35 | keras/saving/model_config.py | 15 | 6 | def model_from_json(json_string, custom_objects=None):
from keras.layers import (
deserialize_from_json,
) # pylint: disable=g-import-not | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | model_from_json | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | model_config.py | 8 | 5 | https://github.com/keras-team/keras.git | 1 | 28 | 0 | 15 | 44 | Python | {
"docstring": "Parses a JSON model configuration string and returns a model instance.\n\n Usage:\n\n >>> model = tf.keras.Sequential([\n ... tf.keras.layers.Dense(5, input_shape=(3,)),\n ... tf.keras.layers.Softmax()])\n >>> config = model.to_json()\n >>> loaded_model = tf.keras.models.model_from_json(config)\n\n Args:\n json_string: JSON string encoding a model configuration.\n custom_objects: Optional dictionary mapping names\n (strings) to custom classes or functions to be\n considered during deserialization.\n\n Returns:\n A Keras model instance (uncompiled).\n ",
"language": "en",
"n_whitespaces": 137,
"n_words": 59,
"vocab_size": 45
} | def model_from_json(json_string, custom_objects=None):
from keras.layers import (
deserialize_from_json,
) # pylint: disable=g-import-not-at-top
return deserialize_from_json(json_string, custom_objects=custom_objects)
|
|
116,987 | 319,727 | 43 | src/documents/tests/test_management_convert_thumbnail.py | 15 | 8 | def test_do_nothing_if_converted(self, run_convert_mock):
stdout, _ = self.call_command()
run_convert_mock.assert_not_called()
self.assertIn("Converting all PNG thumbnails to WebP", stdout)
| Fixes existing testing, adds test coverage of new command | test_do_nothing_if_converted | 08c3d6e84b17da2acfb10250438fe357398e5e0e | paperless-ngx | test_management_convert_thumbnail.py | 8 | 4 | https://github.com/paperless-ngx/paperless-ngx.git | 1 | 30 | 0 | 15 | 53 | Python | {
"docstring": "\n GIVEN:\n - Document exists with default WebP thumbnail path\n WHEN:\n - Thumbnail conversion is attempted\n THEN:\n - Nothing is converted\n ",
"language": "en",
"n_whitespaces": 82,
"n_words": 20,
"vocab_size": 17
} | def test_do_nothing_if_converted(self, run_convert_mock):
stdout, _ = self.call_command()
run_convert_mock.assert_not_called()
self.assertIn("Converting all PNG thumbnails to WebP", stdout)
|
|
3,433 | 20,578 | 140 | pipenv/patched/notpip/_vendor/pyparsing/core.py | 30 | 11 | def __ror__(self, other):
if isinstance(other, str_type):
other = self._literalStringClass(other)
if not isinstance(other, ParserElement):
raise TypeError(
"Cannot combine element of type {} with ParserElement".format(
| check point progress on only bringing in pip==22.0.4 (#4966)
* vendor in pip==22.0.4
* updating vendor packaging version
* update pipdeptree to fix pipenv graph with new version of pip.
* Vendoring of pip-shims 0.7.0
* Vendoring of requirementslib 1.6.3
* Update pip index safety restrictions patch for pip==22.0.4
* Update patches
* exclude pyptoject.toml from black to see if that helps.
* Move this part of the hash collection back to the top (like prior implementation) because it affects the outcome of this test now in pip 22.0.4 | __ror__ | f3166e673fe8d40277b804d35d77dcdb760fc3b3 | pipenv | core.py | 14 | 10 | https://github.com/pypa/pipenv.git | 3 | 52 | 0 | 26 | 86 | Python | {
"docstring": "\n Implementation of ``|`` operator when left operand is not a :class:`ParserElement`\n ",
"language": "en",
"n_whitespaces": 26,
"n_words": 11,
"vocab_size": 11
} | def __ror__(self, other):
if isinstance(other, str_type):
other = self._literalStringClass(other)
if not isinstance(other, ParserElement):
raise TypeError(
"Cannot combine element of type {} with ParserElement".format(
type(other).__name__
)
)
return other | self
|
|
53,616 | 213,062 | 151 | samtranslator/third_party/py27hash/hash.py | 48 | 10 | def shash(value):
length = len(value)
if length == 0:
return 0
x = Hash.ordinal(value[0]) << 7
for c in value:
x = (1000003 * x) ^ Hash.ordinal(c)
x ^= length
x &= 0xFFFFFFFFFFFFFFFF
if x == -1:
x = -2
# Convert to C long type
| fix: Py27hash fix (#2182)
* Add third party py27hash code
* Add Py27UniStr and unit tests
* Add py27hash_fix utils and tests
* Add to_py27_compatible_template and tests
* Apply py27hash fix to wherever it is needed
* Apply py27hash fix, all tests pass except api_with_any_method_in_swagger
* apply py27hash fix in openapi + run black
* remove py27 testing
* remove other py27 references
* black fixes
* fixes/typos
* remove py27 from tox.ini
* refactoring
* third party notice
* black
* Fix py27hash fix to deal with null events
* Fix Py27UniStr repr for unicode literals
* black reformat
* Update _template_has_api_resource to check data type more defensively
* Apply py27Dict in _get_authorizers
* Apply Py27Dict to authorizers and gateway responses which will go into swagger
* Update to_py27_compatible_template to handle parameter_values; Add Py27LongInt class
* Rename _convert_to_py27_dict to _convert_to_py27_type
* Apply Py27UniStr to path param name
* Handle HttpApi resource under to_py27_compatible_template
* Fix InvalidDocumentException to not sort different exceptions
* black reformat
* Remove unnecessary test files
Co-authored-by: Wing Fung Lau <4760060+hawflau@users.noreply.github.com> | shash | a5db070f446b7cfebdaa6ad2e3dcf78f6105a272 | serverless-application-model | hash.py | 11 | 12 | https://github.com/aws/serverless-application-model.git | 4 | 77 | 0 | 35 | 125 | Python | {
"docstring": "\n Returns a Python 2.7 hash for a string.\n\n Logic ported from the 2.7 Python branch: cpython/Objects/stringobject.c\n Method: static long string_hash(PyStringObject *a)\n\n Args:\n value: input string\n\n Returns:\n Python 2.7 hash\n ",
"language": "en",
"n_whitespaces": 94,
"n_words": 29,
"vocab_size": 23
} | def shash(value):
length = len(value)
if length == 0:
return 0
x = Hash.ordinal(value[0]) << 7
for c in value:
x = (1000003 * x) ^ Hash.ordinal(c)
x ^= length
x &= 0xFFFFFFFFFFFFFFFF
if x == -1:
x = -2
# Convert to C long type
return ctypes.c_long(x).value
|
|
@pytest.fixture | 22,138 | 105,508 | 152 | tests/packaged_modules/test_folder_based_builder.py | 74 | 27 | def data_files_with_one_split_and_metadata(tmp_path, auto_text_file):
data_dir = tmp_path / "autofolder_data_dir_with_metadata_one_split"
data_dir.mkdir(parents=True, exist_ok=True)
subdir = data_dir / "subdir"
subdir.mkdir(parents=True, exist_ok=True)
filename = data_dir / "file.txt"
shutil.copyfile(auto_text_file, filename)
filename2 = data_dir / "file2.txt"
shutil.copyfile(auto_text_file, filename2)
filename3 = subdir / "file3.txt" # in subdir
shutil.copyfile(auto_text_file, filename3)
metadata_filename = data_dir / "metadata.jsonl"
metadata = textwrap.dedent(
)
with open(metadata_filename, "w", encoding="utf-8") as f:
f.write(metadata)
data_files_with_one_split_and_metadata = DataFilesDict.from_local_or_remote(
get_data_patterns_locally(data_dir), data_dir
)
assert len(data_files_with_one_split_and_metadata) == 1
assert len(data_files_with_one_split_and_metadata["train"]) == 4
return data_files_with_one_split_and_metadata
@pytest.fixture | Add AudioFolder packaged loader (#4530)
* add audiofolder loader (almost identical to imagefolder except for inferring labels is not default)
* add instruction on how to obtain list of audio extensions
* add a generic loader
* patch autofolder for streaming manually
* align autofolder with the latest imagefolder implementation
* update tests
* add test for duplicate label col
* add tests for autofolder (+copied from imagefolder)
* add missed audio_file fixture
* add documentation
* remove boilerplate, make base feature builder's class arg instead of a config's one
* remove self.config.label_name, use hardcoded 'label'
* patch parents that inherit from DatasetBuilder, revert get_imports
* rename autofolder -> folder_builder
* make base column name an abstract attr of FolderBuilder instead of config's parameter
* Update src/datasets/streaming.py
Co-authored-by: Mario Šaško <mario@huggingface.co>
* rename FolderBuilder -> FolderBasedBuilder
* set drop_labels to None by default for AudioFolder
* update documentation
* check if builder extending for streaming is not in datasets.builder module
Co-authored-by: Mario Šaško <mario@huggingface.co>
Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com> | data_files_with_one_split_and_metadata | 6ea46d88c6a09244d785e55e2681bc4033740442 | datasets | test_folder_based_builder.py | 12 | 27 | https://github.com/huggingface/datasets.git | 1 | 145 | 1 | 48 | 255 | Python | {
"docstring": "\\\n {\"file_name\": \"file.txt\", \"additional_feature\": \"Dummy file\"}\n {\"file_name\": \"file2.txt\", \"additional_feature\": \"Second dummy file\"}\n {\"file_name\": \"subdir/file3.txt\", \"additional_feature\": \"Third dummy file\"}\n ",
"language": "en",
"n_whitespaces": 46,
"n_words": 18,
"vocab_size": 11
} | def data_files_with_one_split_and_metadata(tmp_path, auto_text_file):
data_dir = tmp_path / "autofolder_data_dir_with_metadata_one_split"
data_dir.mkdir(parents=True, exist_ok=True)
subdir = data_dir / "subdir"
subdir.mkdir(parents=True, exist_ok=True)
filename = data_dir / "file.txt"
shutil.copyfile(auto_text_file, filename)
filename2 = data_dir / "file2.txt"
shutil.copyfile(auto_text_file, filename2)
filename3 = subdir / "file3.txt" # in subdir
shutil.copyfile(auto_text_file, filename3)
metadata_filename = data_dir / "metadata.jsonl"
metadata = textwrap.dedent(
)
with open(metadata_filename, "w", encoding="utf-8") as f:
f.write(metadata)
data_files_with_one_split_and_metadata = DataFilesDict.from_local_or_remote(
get_data_patterns_locally(data_dir), data_dir
)
assert len(data_files_with_one_split_and_metadata) == 1
assert len(data_files_with_one_split_and_metadata["train"]) == 4
return data_files_with_one_split_and_metadata
@pytest.fixture |
73,371 | 250,293 | 345 | tests/handlers/test_e2e_room_keys.py | 47 | 16 | def test_upload_room_keys_wrong_version(self) -> None:
version = self.get_success(
self.handler.create_version(
self.local_user,
{
"algorithm": "m.megolm_backup.v1",
"auth_data": "first_version_auth_data",
},
)
)
self.assertEqual(version, "1")
version = self.get_success(
self.handler.create_version(
self.local_user,
{
"algorithm": "m.megolm_backup.v1",
"auth_data": "second_version_auth_data",
},
)
)
self.assertEqual | Add missing type hints to tests.handlers. (#14680)
And do not allow untyped defs in tests.handlers. | test_upload_room_keys_wrong_version | 652d1669c5a103b1c20478770c4aaf18849c09a3 | synapse | test_e2e_room_keys.py | 13 | 27 | https://github.com/matrix-org/synapse.git | 1 | 120 | 0 | 30 | 202 | Python | {
"docstring": "Check that we get a 403 on uploading keys for an old version",
"language": "en",
"n_whitespaces": 12,
"n_words": 13,
"vocab_size": 13
} | def test_upload_room_keys_wrong_version(self) -> None:
version = self.get_success(
self.handler.create_version(
self.local_user,
{
"algorithm": "m.megolm_backup.v1",
"auth_data": "first_version_auth_data",
},
)
)
self.assertEqual(version, "1")
version = self.get_success(
self.handler.create_version(
self.local_user,
{
"algorithm": "m.megolm_backup.v1",
"auth_data": "second_version_auth_data",
},
)
)
self.assertEqual(version, "2")
e = self.get_failure(
self.handler.upload_room_keys(self.local_user, "1", room_keys), SynapseError
)
res = e.value.code
self.assertEqual(res, 403)
|
|
20,913 | 101,501 | 24 | lib/gui/utils.py | 10 | 11 | def previewtrain(self) -> Dict[str, List[Union[Image.Image, ImageTk.PhotoImage, None, float]]]:
return self._previewtrain
| Bugfix: Preview for extract in batch mode | previewtrain | dc18c74eea0c7837a820d27628cb12b0824fa30e | faceswap | utils.py | 8 | 10 | https://github.com/deepfakes/faceswap.git | 1 | 33 | 0 | 10 | 48 | Python | {
"docstring": " dict or ``None``: The training preview images. Dictionary key is the image name\n (`str`). Dictionary values are a `list` of the training image (:class:`PIL.Image`), the\n image formatted for tkinter display (:class:`PIL.ImageTK.PhotoImage`), the last\n modification time of the image (`float`).\n\n The value of this property is ``None`` if training is not running or there are no preview\n images available.\n ",
"language": "en",
"n_whitespaces": 101,
"n_words": 58,
"vocab_size": 40
} | def previewtrain(self) -> Dict[str, List[Union[Image.Image, ImageTk.PhotoImage, None, float]]]:
return self._previewtrain
|
|
12,083 | 60,305 | 204 | code/deep/BJMMD/caffe/python/caffe/test/test_coord_map.py | 71 | 18 | def test_padding(self):
| Balanced joint maximum mean discrepancy for deep transfer learning | test_padding | cc4d0564756ca067516f71718a3d135996525909 | transferlearning | test_coord_map.py | 9 | 16 | https://github.com/jindongwang/transferlearning.git | 1 | 165 | 0 | 36 | 254 | Python | {
"docstring": "\n Padding conv adds offset while padding deconv subtracts offset.\n ",
"language": "en",
"n_whitespaces": 24,
"n_words": 9,
"vocab_size": 9
} | def test_padding(self):
n = coord_net_spec()
ax, a, b = coord_map_from_to(n.deconv, n.data)
pad = random.randint(0, 10)
# conv padding
n = coord_net_spec(pad=pad)
_, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
self.assertEquals(a, a_pad)
self.assertEquals(b - pad, b_pad)
# deconv padding
n = coord_net_spec(dpad=pad)
_, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
self.assertEquals(a, a_pad)
self.assertEquals(b + pad, b_pad)
# pad both to cancel out
n = coord_net_spec(pad=pad, dpad=pad)
_, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
self.assertEquals(a, a_pad)
self.assertEquals(b, b_pad)
|
|
121,060 | 337,458 | 14 | src/accelerate/test_utils/testing.py | 8 | 7 | def require_cuda(test_case):
return unittest.skipUnless(torch.cuda.is_a | Clean up tests + fix import (#330) | require_cuda | e5c17f36a8b5bf8b9478d416c4a80841a353fb19 | accelerate | testing.py | 11 | 2 | https://github.com/huggingface/accelerate.git | 1 | 24 | 0 | 8 | 43 | Python | {
"docstring": "\n Decorator marking a test that requires CUDA. These tests are skipped when there are no GPU available.\n ",
"language": "en",
"n_whitespaces": 24,
"n_words": 17,
"vocab_size": 16
} | def require_cuda(test_case):
return unittest.skipUnless(torch.cuda.is_available(), "test requires a GPU")(test_case)
|
|
72,191 | 248,286 | 66 | synapse/logging/handlers.py | 19 | 7 | def _flush_periodically(self) -> None:
while self._active:
# flush is thread-safe; it acquires and releases the lock internally
self.flush()
time.sleep(self._flush_period)
| Another batch of type annotations (#12726) | _flush_periodically | aec69d2481e9ea1d8ea1c0ffce1706a65a7896a8 | synapse | handlers.py | 10 | 7 | https://github.com/matrix-org/synapse.git | 2 | 26 | 0 | 19 | 47 | Python | {
"docstring": "\n Whilst this handler is active, flush the handler periodically.\n ",
"language": "en",
"n_whitespaces": 24,
"n_words": 9,
"vocab_size": 8
} | def _flush_periodically(self) -> None:
while self._active:
# flush is thread-safe; it acquires and releases the lock internally
self.flush()
time.sleep(self._flush_period)
|
|
43,183 | 180,503 | 150 | gradio/components.py | 28 | 11 | def save_flagged(self, dir, label, data, encryption_key) -> str | Dict:
if "confidences" in data:
return json.dumps(
{
example["label"]: example["confidence"]
| Live website changes (#1578)
* fix audio output cache (#804)
* fix audio output cache
* changes
* version update
Co-authored-by: Ali Abid <aliabid94@gmail.com>
* Website Tracker Slackbot (#797)
* added commands to reload script
* catch errors with git pull
* read new webhook from os variable
* correcting bash
* bash fixes
* formatting
* more robust error checking
* only sends success if git changes
* catching error from script
* escaping error text to send with curl
* correct text escaping for error message
* fix search bug in guides (#809)
* Update getting_started.md (#808)
* Fix type of server returned by `Launchable` (#810)
* `Launchable` returns a FastAPI now
* Update .gitignore
* Add a missing line to getting started (#816)
Former-commit-id: 81e271ca22e838e1ee618d48cdb0e904fd233cf3 [formerly 96f203108bf1222fe333a0175687293abdc669d7]
Former-commit-id: eaff13262853078e0c6c0baa54c731d9e56bc73f
* Add a missing line to getting started (#816)
Former-commit-id: 81e271ca22e838e1ee618d48cdb0e904fd233cf3 [formerly 81e271ca22e838e1ee618d48cdb0e904fd233cf3 [formerly 96f203108bf1222fe333a0175687293abdc669d7]]
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* Add a missing line to getting started (#816)
Former-commit-id: 81e271ca22e838e1ee618d48cdb0e904fd233cf3 [formerly 81e271ca22e838e1ee618d48cdb0e904fd233cf3 [formerly 81e271ca22e838e1ee618d48cdb0e904fd233cf3 [formerly 96f203108bf1222fe333a0175687293abdc669d7]]]
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Former-commit-id: bce6f9c4c5254301eb73e76eb47cddab3e132c24
* Add a missing line to getting started (#816)
Former-commit-id: 81e271ca22e838e1ee618d48cdb0e904fd233cf3 [formerly 81e271ca22e838e1ee618d48cdb0e904fd233cf3 [formerly 81e271ca22e838e1ee618d48cdb0e904fd233cf3 [formerly 81e271ca22e838e1ee618d48cdb0e904fd233cf3 [formerly 96f203108bf1222fe333a0175687293abdc669d7]]]]
Former-commit-id: eaff13262853078e0c6c0baa54c731d9e56bc73f
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Former-commit-id: feba0888e3d488b82a3518343f607517d0836f13
* Add a missing line to getting started (#816)
* Clean-History
- Remove 51MB file with this commit
Former-commit-id: 34b6a2325d613eeef622410f2d1ff3d869d3133c
* Clean-History
- Remove 51MB file with this commit
Former-commit-id: 34b6a2325d613eeef622410f2d1ff3d869d3133c
Former-commit-id: dd700c33cca3f560621219530444b631b7767392
* Clean-History
- Remove 51MB file with this commit
Former-commit-id: 34b6a2325d613eeef622410f2d1ff3d869d3133c
Former-commit-id: dd700c33cca3f560621219530444b631b7767392
Former-commit-id: 0d80e6a056abad1c4d1fd6f162eb725e0db5fb4f
* Clean-History
- Remove 51MB file with this commit
Former-commit-id: 34b6a2325d613eeef622410f2d1ff3d869d3133c
Former-commit-id: dd700c33cca3f560621219530444b631b7767392
Former-commit-id: 0d80e6a056abad1c4d1fd6f162eb725e0db5fb4f
Former-commit-id: 20523b05194438209cf64cb688008b4599eb847e
* changes
* changes
* Homepage: header image size (#1347)
* image size
* image in local assets
* add dall-e mini banner
* undo ui changes
* changes
* changes
* updates
* updates
* changes
* changes
* changes
* h11 dependency
* add npm build-mac
* expand demo button to all classes
* add demos to docstrings
* add anchor tags to headers
* add required tag to param table
* add consistent styling for headers
* skip param beginning with underscore from docs
* skip kwargs param from docs
* remove types in param docstring
* override signature to reflect usage
* add supported events
* add step-by-step guides
* fix guide contribution link
* add related spaces
* fix img styling on guides
* pin quickstart, advanced, and block guides to top
* margin fix
* autogenerated copy buttons for all codeblocks
* changes
* documentaiton
* format
* launch
* formatting
* style changes
* remove backticks
* changes
* changes
Co-authored-by: Ali Abid <aliabid94@gmail.com>
Co-authored-by: Ali Abdalla <ali.si3luwa@gmail.com>
Co-authored-by: Julien Chaumond <julien@huggingface.co>
Co-authored-by: Ömer Faruk Özdemir <farukozderim@gmail.com>
Co-authored-by: Ali <ali.abid@huggingface.co>
Co-authored-by: Victor Muštar <victor.mustar@gmail.com>
Co-authored-by: Abubakar Abid <abubakar@huggingface.co> | save_flagged | 70ebf698fa75ad094a2ba52cd1de645fa58eff85 | gradio | components.py | 13 | 14 | https://github.com/gradio-app/gradio.git | 3 | 54 | 0 | 26 | 90 | Python | {
"docstring": "\n Returns:\n Either a string representing the main category label, or a dictionary with category keys mapping to confidence levels.\n ",
"language": "en",
"n_whitespaces": 45,
"n_words": 19,
"vocab_size": 17
} | def save_flagged(self, dir, label, data, encryption_key) -> str | Dict:
if "confidences" in data:
return json.dumps(
{
example["label"]: example["confidence"]
for example in data["confidences"]
}
)
else:
return data["label"]
|
|
30,091 | 133,740 | 583 | rllib/agents/impala/tests/test_vtrace.py | 96 | 23 | def test_higher_rank_inputs_for_importance_weights(self):
for fw in framework_iterator(frameworks=("torch", "tf"), session=True):
vtrace = vtrace_tf if fw != "torch" else vtrace_torch
if fw == | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | test_higher_rank_inputs_for_importance_weights | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | ray | test_vtrace.py | 18 | 29 | https://github.com/ray-project/ray.git | 4 | 315 | 0 | 47 | 447 | Python | {
"docstring": "Checks support for additional dimensions in inputs.",
"language": "en",
"n_whitespaces": 6,
"n_words": 7,
"vocab_size": 7
} | def test_higher_rank_inputs_for_importance_weights(self):
for fw in framework_iterator(frameworks=("torch", "tf"), session=True):
vtrace = vtrace_tf if fw != "torch" else vtrace_torch
if fw == "tf":
inputs_ = {
"log_rhos": tf1.placeholder(
dtype=tf.float32, shape=[None, None, 1]
),
"discounts": tf1.placeholder(
dtype=tf.float32, shape=[None, None, 1]
),
"rewards": tf1.placeholder(
dtype=tf.float32, shape=[None, None, 42]
),
"values": tf1.placeholder(dtype=tf.float32, shape=[None, None, 42]),
"bootstrap_value": tf1.placeholder(
dtype=tf.float32, shape=[None, 42]
),
}
else:
inputs_ = {
"log_rhos": Box(-1.0, 1.0, (8, 10, 1)).sample(),
"discounts": Box(-1.0, 1.0, (8, 10, 1)).sample(),
"rewards": Box(-1.0, 1.0, (8, 10, 42)).sample(),
"values": Box(-1.0, 1.0, (8, 10, 42)).sample(),
"bootstrap_value": Box(-1.0, 1.0, (10, 42)).sample(),
}
output = vtrace.from_importance_weights(**inputs_)
check(int(output.vs.shape[-1]), 42)
|
|
9,232 | 47,727 | 314 | tests/www/views/test_views_tasks.py | 104 | 46 | def test_task_fail_duration(app, admin_client, dag_maker, session):
with dag_maker() as dag:
op1 = BashOperator(task_id='fail', bash_command='exit 1')
op2 = BashOperator(task_id='success', bash_command='exit 0')
with pytest.raises(AirflowException):
op1.run()
op2.run()
op1_fails = (
session.query(TaskFail)
.filter(
TaskFail.task_id == 'fail',
TaskFail.dag_id == dag.dag_id,
)
.all()
)
op2_fails = (
session.query(TaskFail)
.filter(
TaskFail.task_id == 'success',
TaskFail.dag_id == dag.dag_id,
)
.all()
)
assert len(op1_fails) == 1
assert len(op2_fails) == 0
with unittest.mock.patch.object(app, 'dag_bag') as mocked_dag_bag:
mocked_dag_bag.get_dag.return_valu | Fix TaskFail queries in views after run_id migration (#23008)
Two problems here:
1. TaskFail no longer has a executin_date property -- switch to run_id
2. We weren't joining to DagRun correctly, meaning we'd end up with a
cross-product effect(? Something weird anyway)
Co-authored-by: Karthikeyan Singaravelan <tir.karthi@gmail.com> | test_task_fail_duration | 70049f19e4ac82ea922d7e59871a3b4ebae068f1 | airflow | test_views_tasks.py | 15 | 34 | https://github.com/apache/airflow.git | 3 | 268 | 0 | 63 | 458 | Python | {
"docstring": "Task duration page with a TaskFail entry should render without error.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | def test_task_fail_duration(app, admin_client, dag_maker, session):
with dag_maker() as dag:
op1 = BashOperator(task_id='fail', bash_command='exit 1')
op2 = BashOperator(task_id='success', bash_command='exit 0')
with pytest.raises(AirflowException):
op1.run()
op2.run()
op1_fails = (
session.query(TaskFail)
.filter(
TaskFail.task_id == 'fail',
TaskFail.dag_id == dag.dag_id,
)
.all()
)
op2_fails = (
session.query(TaskFail)
.filter(
TaskFail.task_id == 'success',
TaskFail.dag_id == dag.dag_id,
)
.all()
)
assert len(op1_fails) == 1
assert len(op2_fails) == 0
with unittest.mock.patch.object(app, 'dag_bag') as mocked_dag_bag:
mocked_dag_bag.get_dag.return_value = dag
resp = admin_client.get(f"dags/{dag.dag_id}/duration", follow_redirects=True)
html = resp.get_data().decode()
cumulative_chart = json.loads(re.search("data_cumlinechart=(.*);", html).group(1))
line_chart = json.loads(re.search("data_linechart=(.*);", html).group(1))
assert resp.status_code == 200
assert sorted(item["key"] for item in cumulative_chart) == ["fail", "success"]
assert sorted(item["key"] for item in line_chart) == ["fail", "success"]
|
|
71,062 | 246,168 | 137 | tests/rest/admin/test_user.py | 30 | 16 | def test_all_users(self) -> None:
self._create_users(2)
channel = self.make_request(
"GET",
self.url + " | Add type hints to `tests/rest/admin` (#11851) | test_all_users | 901b264c0c88f39cbfb8b2229e0dc57968882658 | synapse | test_user.py | 11 | 15 | https://github.com/matrix-org/synapse.git | 1 | 96 | 0 | 29 | 157 | Python | {
"docstring": "\n List all users, including deactivated users.\n ",
"language": "en",
"n_whitespaces": 21,
"n_words": 6,
"vocab_size": 6
} | def test_all_users(self) -> None:
self._create_users(2)
channel = self.make_request(
"GET",
self.url + "?deactivated=true",
{},
access_token=self.admin_user_tok,
)
self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body)
self.assertEqual(3, len(channel.json_body["users"]))
self.assertEqual(3, channel.json_body["total"])
# Check that all fields are available
self._check_fields(channel.json_body["users"])
|
|
56,586 | 222,485 | 69 | python3.10.4/Lib/difflib.py | 31 | 12 | def real_quick_ratio(self):
la, lb = len(self.a), len(self.b)
# can't have more matches than the number of elements in the
# shorter sequence
return _calculate_ratio(min(la, lb), la + lb)
__class_getitem__ = classmethod(GenericAlias)
| add python 3.10.4 for windows | real_quick_ratio | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | difflib.py | 10 | 3 | https://github.com/XX-net/XX-Net.git | 1 | 37 | 0 | 28 | 72 | Python | {
"docstring": "Return an upper bound on ratio() very quickly.\n\n This isn't defined beyond that it is an upper bound on .ratio(), and\n is faster to compute than either .ratio() or .quick_ratio().\n ",
"language": "en",
"n_whitespaces": 51,
"n_words": 30,
"vocab_size": 25
} | def real_quick_ratio(self):
la, lb = len(self.a), len(self.b)
# can't have more matches than the number of elements in the
# shorter sequence
return _calculate_ratio(min(la, lb), la + lb)
__class_getitem__ = classmethod(GenericAlias)
|
|
15,672 | 71,415 | 201 | wagtail/admin/tests/pages/test_bulk_actions/test_bulk_unpublish.py | 35 | 9 | def test_unpublish_view_invalid_page_id(self):
# Request confirm unpublish page but with illegal page id
response = self.client.get(
reverse(
"wagtail_bulk_action",
args=(
"wagtailcore",
"page",
"unpublish",
),
)
)
# Check that the user receiv | Reformat with black | test_unpublish_view_invalid_page_id | d10f15e55806c6944827d801cd9c2d53f5da4186 | wagtail | test_bulk_unpublish.py | 13 | 12 | https://github.com/wagtail/wagtail.git | 1 | 41 | 0 | 31 | 73 | Python | {
"docstring": "\n This tests that the unpublish view returns an error if the page id is invalid\n ",
"language": "en",
"n_whitespaces": 30,
"n_words": 15,
"vocab_size": 14
} | def test_unpublish_view_invalid_page_id(self):
# Request confirm unpublish page but with illegal page id
response = self.client.get(
reverse(
"wagtail_bulk_action",
args=(
"wagtailcore",
"page",
"unpublish",
),
)
)
# Check that the user received a 404 response
self.assertEqual(response.status_code, 404)
|
|
30,250 | 134,305 | 59 | python/ray/train/tests/test_session.py | 23 | 14 | def test_warn_report():
fn = report
with warnings.catch_warnings( | [AIR] Hard deprecate train.report, warn on air.session misuse (#29613)
Signed-off-by: Antoni Baum antoni.baum@protonmail.com
Hard deprecates `ray.train.report` and other session functions and ensures that the user is informed when using `ray.air.session` if they are not in session for consistency with the old functions. | test_warn_report | 9b29fd6501ff0e3e69d0333bf214482b86f9e97f | ray | test_session.py | 12 | 7 | https://github.com/ray-project/ray.git | 1 | 60 | 0 | 22 | 104 | Python | {
"docstring": "Checks if calling session.report function outside of session raises warning.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | def test_warn_report():
fn = report
with warnings.catch_warnings(record=True) as record:
# Ignore Deprecation warnings.
warnings.filterwarnings("ignore", category=DeprecationWarning)
assert not fn(dict())
assert fn.__name__ in record[0].message.args[0]
reset_log_once_with_str(fn.__name__)
|
|
70,258 | 244,142 | 828 | mmdet/models/dense_heads/mask2former_head.py | 201 | 54 | def forward(self, feats, img_metas):
batch_size = len(img_metas)
mask_features, multi_scale_memorys = self.pixel_decoder(feats)
# multi_scale_memorys (from low resolution to high resolution)
decoder_inputs = []
decoder_positional_encodings = []
for i in range(self.num_transformer_feat_level):
decoder_input = self.decoder_input_projs[i](multi_scale_memorys[i])
# shape (batch_size, c, h, w) -> (h*w, batch_size, c)
decoder_input = decoder_input.flatten(2).permute(2, 0, 1)
level_embed = self.level_embed.weight[i].view(1, 1, -1)
decoder_input = decoder_input + level_embed
# shape (batch_size, c, h, w) -> (h*w, batch_size, c)
mask = decoder_input.new_zeros(
(batch_size, ) + multi_scale_memorys[i].shape[-2:],
dtype=torch.bool)
decoder_positional_encoding = self.decoder_positional_encoding(
mask)
decoder_positional_encoding = decoder_positional_encoding.flatten(
2).permute(2, 0, 1)
decoder_inputs.append(decoder_input)
decoder_positional_encodings.append(decoder_positional_encoding)
# shape (num_queries, c) -> (num_queries, batch_size, c)
query_feat = self.query_feat.weight.unsqueeze(1).repeat(
(1, batch_size, 1))
query_embed = self.query_embed.weight.unsqueeze(1).repeat(
(1, batch_size, 1))
cls_pred_list = []
mask_pred_list = []
cls_pred, mask_pred, attn_mask = self.forward_head(
query_feat, mask_features, multi_scale_memorys[0].shape[-2:])
cls_pred_list.append(cls_pred)
mask_pred_list.append(mask_pred)
for i in range(self.num_transformer_decoder_layers):
level_idx = i % self.num_transformer_feat_level
# if a mask is all True(all background), then set it all False.
attn_mask[torch.where(
attn_mask.sum(-1) == at | [Feature] Add Mask2Former to mmdet (#6938)
update doc
update doc format
deepcopy pixel_decoder cfg
move mask_pseudo_sampler cfg to config file
move part of postprocess from head to detector
fix bug in postprocessing
move class setting from head to config file
remove if else
move mask2bbox to mask/util
update docstring
update docstring in result2json
fix bug
update class_weight
add maskformer_fusion_head
add maskformer fusion head
update
add cfg for filter_low_score
update maskformer
update class_weight
update config
update unit test
rename param
update comments in config
rename variable, rm arg, update unit tests
update mask2bbox
add unit test for mask2bbox
replace unsqueeze(1) and squeeze(1)
add unit test for maskformer_fusion_head
update docstrings
update docstring
delete \
remove modification to ce loss
update docstring
update docstring
update docstring of ce loss
update unit test
update docstring
update docstring
update docstring
rename
rename
add msdeformattn pixel decoder
maskformer refactor
add strides in config
remove redundant code
remove redundant code
update unit test
update config
update | forward | 14f0e9585c15c28f0c31dcc3ea352449bbe5eb96 | mmdetection | mask2former_head.py | 16 | 50 | https://github.com/open-mmlab/mmdetection.git | 3 | 412 | 0 | 121 | 632 | Python | {
"docstring": "Forward function.\n\n Args:\n feats (list[Tensor]): Multi scale Features from the\n upstream network, each is a 4D-tensor.\n img_metas (list[dict]): List of image information.\n\n Returns:\n tuple: A tuple contains two elements.\n\n - cls_pred_list (list[Tensor)]: Classification logits \\\n for each decoder layer. Each is a 3D-tensor with shape \\\n (batch_size, num_queries, cls_out_channels). \\\n Note `cls_out_channels` should includes background.\n - mask_pred_list (list[Tensor]): Mask logits for each \\\n decoder layer. Each with shape (batch_size, num_queries, \\\n h, w).\n ",
"language": "en",
"n_whitespaces": 240,
"n_words": 73,
"vocab_size": 54
} | def forward(self, feats, img_metas):
batch_size = len(img_metas)
mask_features, multi_scale_memorys = self.pixel_decoder(feats)
# multi_scale_memorys (from low resolution to high resolution)
decoder_inputs = []
decoder_positional_encodings = []
for i in range(self.num_transformer_feat_level):
decoder_input = self.decoder_input_projs[i](multi_scale_memorys[i])
# shape (batch_size, c, h, w) -> (h*w, batch_size, c)
decoder_input = decoder_input.flatten(2).permute(2, 0, 1)
level_embed = self.level_embed.weight[i].view(1, 1, -1)
decoder_input = decoder_input + level_embed
# shape (batch_size, c, h, w) -> (h*w, batch_size, c)
mask = decoder_input.new_zeros(
(batch_size, ) + multi_scale_memorys[i].shape[-2:],
dtype=torch.bool)
decoder_positional_encoding = self.decoder_positional_encoding(
mask)
decoder_positional_encoding = decoder_positional_encoding.flatten(
2).permute(2, 0, 1)
decoder_inputs.append(decoder_input)
decoder_positional_encodings.append(decoder_positional_encoding)
# shape (num_queries, c) -> (num_queries, batch_size, c)
query_feat = self.query_feat.weight.unsqueeze(1).repeat(
(1, batch_size, 1))
query_embed = self.query_embed.weight.unsqueeze(1).repeat(
(1, batch_size, 1))
cls_pred_list = []
mask_pred_list = []
cls_pred, mask_pred, attn_mask = self.forward_head(
query_feat, mask_features, multi_scale_memorys[0].shape[-2:])
cls_pred_list.append(cls_pred)
mask_pred_list.append(mask_pred)
for i in range(self.num_transformer_decoder_layers):
level_idx = i % self.num_transformer_feat_level
# if a mask is all True(all background), then set it all False.
attn_mask[torch.where(
attn_mask.sum(-1) == attn_mask.shape[-1])] = False
# cross_attn + self_attn
layer = self.transformer_decoder.layers[i]
attn_masks = [attn_mask, None]
query_feat = layer(
query=query_feat,
key=decoder_inputs[level_idx],
value=decoder_inputs[level_idx],
query_pos=query_embed,
key_pos=decoder_positional_encodings[level_idx],
attn_masks=attn_masks,
query_key_padding_mask=None,
# here we do not apply masking on padded region
key_padding_mask=None)
cls_pred, mask_pred, attn_mask = self.forward_head(
query_feat, mask_features, multi_scale_memorys[
(i + 1) % self.num_transformer_feat_level].shape[-2:])
cls_pred_list.append(cls_pred)
mask_pred_list.append(mask_pred)
return cls_pred_list, mask_pred_list
|
|
50,383 | 203,453 | 627 | django/contrib/admin/options.py | 139 | 36 | def formfield_for_manytomany(self, db_field, request, **kwargs):
# If it uses an intermediary model that isn't auto created, don't show
# a field in admin.
if not db_field.remote_field.through._meta.auto_created:
return None
db = kwargs.get("using")
if "widget" not in kwargs:
autocomplete_fields = self.get_autocomplete_fields(request)
if db_field.name in autocomplete_fields:
kwargs["widget"] = AutocompleteSelectMultiple(
db_field,
self.admin_site,
using=db,
)
elif db_field.name in self.raw_id_fields:
kwargs["widget"] = widgets.ManyToManyRawIdWidget(
db_field.remote_field,
self.admin_site,
using=db,
)
elif db_field.name in [*self.filter_vertical, *self.filter_horizontal]:
kwargs["widget"] = widgets.FilteredSelectMultiple(
db_field.verbose_name, db_field.name in self.filter_vertical
)
if "queryset" not in kwargs:
queryset = self.get_field_queryset(db, db_field, request)
if | Refs #33476 -- Reformatted code with Black. | formfield_for_manytomany | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | django | options.py | 15 | 38 | https://github.com/django/django.git | 11 | 237 | 0 | 89 | 376 | Python | {
"docstring": "\n Get a form Field for a ManyToManyField.\n ",
"language": "en",
"n_whitespaces": 22,
"n_words": 7,
"vocab_size": 6
} | def formfield_for_manytomany(self, db_field, request, **kwargs):
# If it uses an intermediary model that isn't auto created, don't show
# a field in admin.
if not db_field.remote_field.through._meta.auto_created:
return None
db = kwargs.get("using")
if "widget" not in kwargs:
autocomplete_fields = self.get_autocomplete_fields(request)
if db_field.name in autocomplete_fields:
kwargs["widget"] = AutocompleteSelectMultiple(
db_field,
self.admin_site,
using=db,
)
elif db_field.name in self.raw_id_fields:
kwargs["widget"] = widgets.ManyToManyRawIdWidget(
db_field.remote_field,
self.admin_site,
using=db,
)
elif db_field.name in [*self.filter_vertical, *self.filter_horizontal]:
kwargs["widget"] = widgets.FilteredSelectMultiple(
db_field.verbose_name, db_field.name in self.filter_vertical
)
if "queryset" not in kwargs:
queryset = self.get_field_queryset(db, db_field, request)
if queryset is not None:
kwargs["queryset"] = queryset
form_field = db_field.formfield(**kwargs)
if isinstance(form_field.widget, SelectMultiple) and not isinstance(
form_field.widget, (CheckboxSelectMultiple, AutocompleteSelectMultiple)
):
msg = _(
"Hold down “Control”, or “Command” on a Mac, to select more than one."
)
help_text = form_field.help_text
form_field.help_text = (
format_lazy("{} {}", help_text, msg) if help_text else msg
)
return form_field
|
|
73,276 | 250,109 | 374 | tests/storage/test_cleanup_extrems.py | 57 | 8 | def test_expiry_logic(self) -> None:
self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion[
"1"
] = 100000
self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion[
"2"
] = 200000
self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion[
"3"
] = 300000
self.event_creator_handler._expire_rooms_to_exclude_from_dummy_event_insertion()
# All entries within time frame
self.assertEqual(
len(
self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion
),
3,
)
# Oldest room to expire
self.pump(1.01)
self.event_creator_handler._expire_rooms_to_exclude_from_dummy_event_insertion()
self.assertEqual(
len(
self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion
),
2,
)
# All rooms to expire
self.pump(2)
self.assertEqual(
| Require types in tests.storage. (#14646)
Adds missing type hints to `tests.storage` package
and does not allow untyped definitions. | test_expiry_logic | 3ac412b4e2f8c5ba11dc962b8a9d871c1efdce9b | synapse | test_cleanup_extrems.py | 11 | 35 | https://github.com/matrix-org/synapse.git | 1 | 114 | 0 | 35 | 186 | Python | {
"docstring": "Simple test to ensure that _expire_rooms_to_exclude_from_dummy_event_insertion()\n expires old entries correctly.\n ",
"language": "en",
"n_whitespaces": 24,
"n_words": 10,
"vocab_size": 10
} | def test_expiry_logic(self) -> None:
self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion[
"1"
] = 100000
self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion[
"2"
] = 200000
self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion[
"3"
] = 300000
self.event_creator_handler._expire_rooms_to_exclude_from_dummy_event_insertion()
# All entries within time frame
self.assertEqual(
len(
self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion
),
3,
)
# Oldest room to expire
self.pump(1.01)
self.event_creator_handler._expire_rooms_to_exclude_from_dummy_event_insertion()
self.assertEqual(
len(
self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion
),
2,
)
# All rooms to expire
self.pump(2)
self.assertEqual(
len(
self.event_creator_handler._rooms_to_exclude_from_dummy_event_insertion
),
0,
)
|