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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 101 | f65417656ba8c59438d832b6e2a431f78d40c21c | https://github.com/pandas-dev/pandas.git | def rolling(self, *args, **kwargs) -> RollingGroupby:
from pandas.core.window import RollingGroupby
| 48 | 17 | pandas | groupby.py | pandas/core/groupby/groupby.py | 9 | 40,113 | 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,
)
| 12 | rolling | 167,770 | 13 | 71 | TYP: more return annotations in core/ (#47618)
* TYP: more return annotations in core/
* from __future__ import annotations
* more __future__ | {
"docstring": "\n Return a rolling grouper, providing rolling functionality per group.\n ",
"language": "en",
"n_whitespaces": 24,
"n_words": 9,
"vocab_size": 8
} | 1 | Python | 18 |
|
0 | 417 | 2a05ccdb07cff88e56661dee8a9271859354027f | https://github.com/networkx/networkx.git | 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 | 240 | 97 | networkx | degree_seq.py | networkx/generators/degree_seq.py | 17 | 42,064 | 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
| 100 | expected_degree_graph | 176,730 | 35 | 375 | 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> | {
"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
} | 13 | Python | 179 |
|
0 | 208 | 4c58179509e6f6047789efb0a95c2b0e20cb6c8f | https://github.com/mlflow/mlflow.git | def save(self, path):
os.makedirs(path, | 153 | 36 | mlflow | base.py | mlflow/models/evaluation/base.py | 13 | 2,897 | 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))
| 17 | save | 19,151 | 22 | 253 | 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> | {
"docstring": "Write the evaluation results to the specified local filesystem path",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 9
} | 3 | Python | 49 |
|
0 | 154 | 3255fa4ebb9fbc1df6bb063c0eb77a0298ca8f72 | https://github.com/getsentry/sentry.git | 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"] = | 137 | 38 | sentry | test_message_builder.py | tests/sentry/integrations/slack/test_message_builder.py | 12 | 18,592 | 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
| 14 | test_build_group_generic_issue_attachment | 89,933 | 25 | 249 | 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 | {
"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
} | 1 | Python | 51 |
|
0 | 127 | b3bc4e734528d3b186c3a38a6e73e106c3555cc7 | https://github.com/iperov/DeepFaceLive.git | 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:
| 82 | 34 | DeepFaceLive | ImageProcessor.py | xlib/image/ImageProcessor.py | 13 | 42,906 | 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
| 21 | apply | 179,114 | 14 | 137 | ImageProcessor.py refactoring | {
"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
} | 3 | Python | 45 |
|
0 | 110 | 843dba903757d592f7703a83ebd75eb3ffb46f6f | https://github.com/microsoft/recommenders.git | def predict(self, x):
# start the timer
self.timer.start()
v_, _ = self | 65 | 30 | recommenders | rbm.py | recommenders/models/rbm/rbm.py | 12 | 7,073 | 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
| 7 | predict | 39,007 | 17 | 111 | removed time from returning args | {
"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
} | 1 | Python | 38 |
|
0 | 74 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | https://github.com/XX-net/XX-Net.git | 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 | 48 | 21 | XX-Net | decoder.py | python3.10.4/Lib/json/decoder.py | 11 | 55,394 | 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
| 6 | raw_decode | 218,569 | 11 | 76 | add python 3.10.4 for windows | {
"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
} | 2 | Python | 24 |
|
1 | 45 | aa1f40a93a882db304e9a06c2a11d93b2532d80a | https://github.com/networkx/networkx.git | def has_bridges(G, root=None):
try:
next(bridges | 28 | 13 | networkx | bridges.py | networkx/algorithms/bridges.py | 11 | @not_implemented_for("multigraph")
@not_implemented_for("directed") | 41,965 | 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") | 7 | has_bridges | 176,561 | 7 | 70 | Improve bridges documentation (#5519)
* Fix bridges documentation
* Revert source code modification
* Revert raise errors for multigraphs | {
"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
} | 2 | Python | 14 |
0 | 112 | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | https://github.com/jindongwang/transferlearning.git | 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 | 49 | 57 | transferlearning | wheel.py | .venv/lib/python3.8/site-packages/pip/_internal/utils/wheel.py | 12 | 12,520 | 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)
| 8 | wheel_metadata | 61,338 | 13 | 103 | upd; format | {
"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
} | 2 | Python | 65 |
|
0 | 172 | e35be138148333078284b942ccc9ed7b1d826f97 | https://github.com/huggingface/datasets.git | 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 | 96 | 29 | datasets | table.py | src/datasets/table.py | 16 | 21,852 | 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)
| 12 | remove_column | 104,416 | 14 | 145 | 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> | {
"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
} | 4 | Python | 40 |
|
0 | 53 | 3a461d02793e6f9d41c2b1a92647e691de1abaac | https://github.com/netbox-community/netbox.git | 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()
| 57 | 13 | netbox | test_models.py | netbox/dcim/tests/test_models.py | 11 | 77,897 | 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()
| 5 | test_cable_cannot_terminate_to_a_wireless_interface | 264,886 | 18 | 95 | Update Cable instantiations to match new signature | {
"docstring": "\n A cable cannot terminate to a wireless interface\n ",
"language": "en",
"n_whitespaces": 23,
"n_words": 8,
"vocab_size": 8
} | 1 | Python | 14 |
|
0 | 114 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | https://github.com/django/django.git | 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 | 35 | 38 | django | creation.py | django/db/backends/base/creation.py | 11 | 50,917 | 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),
}
| 6 | get_test_db_clone_settings | 204,838 | 7 | 63 | Refs #33476 -- Reformatted code with Black. | {
"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
} | 1 | Python | 43 |
|
0 | 52 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | https://github.com/XX-net/XX-Net.git | 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')
| 50 | 14 | XX-Net | imaplib.py | python3.10.4/Lib/imaplib.py | 9 | 55,005 | 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')
| 5 | open | 217,907 | 10 | 83 | add python 3.10.4 for windows | {
"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
} | 1 | Python | 17 |
|
0 | 42 | 7f27e70440c177b2a047b7f74a78ed5cd5b4b596 | https://github.com/Textualize/textual.git | def synchronized_output_end_sequence(self) -> str:
if self.synchronised_output:
return | 25 | 9 | textual | _terminal_features.py | src/textual/_terminal_features.py | 10 | 44,257 | def synchronized_output_end_sequence(self) -> str:
if self.synchronised_output:
return TERMINAL_MODES_ANSI_SEQUENCES[Mode.SynchronizedOutput]["end_sync"]
return ""
| 13 | synchronized_output_end_sequence | 183,574 | 7 | 45 | [terminal buffering] Address PR feedback | {
"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
} | 2 | Python | 10 |
|
0 | 98 | f6021faf2a8e62f88a8d6979ce812dcb71133a8f | https://github.com/jaakkopasanen/AutoEq.git | def _band_penalty_coefficients(self, fc, q, gain, filter_frs):
ref_frs = biquad.digital_coeffs(self.frequenc | 121 | 34 | AutoEq | frequency_response.py | frequency_response.py | 12 | 39,253 | 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))
| 8 | _band_penalty_coefficients | 162,681 | 23 | 176 | 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. | {
"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
} | 1 | Python | 42 |
|
0 | 201 | 02b04cb3ecfc5fce1f627281c312753f3b4b8494 | https://github.com/scikit-learn/scikit-learn.git | 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 | 357 | 48 | scikit-learn | test_voting.py | sklearn/ensemble/tests/test_voting.py | 12 | 76,664 | 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])
| 23 | test_predict_on_toy_problem | 261,153 | 22 | 469 | 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> | {
"docstring": "Manually check predicted class labels for toy dataset.",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 8
} | 1 | Python | 104 |
|
0 | 29 | 5a850eb044ca07f1f3bcb1b284116d6f2d37df1b | https://github.com/scikit-learn/scikit-learn.git | def fit_transform(self, X, y=None):
self._validate_params()
return self._tran | 28 | 8 | scikit-learn | _dict_vectorizer.py | sklearn/feature_extraction/_dict_vectorizer.py | 8 | 76,257 | def fit_transform(self, X, y=None):
self._validate_params()
return self._transform(X, fitting=True)
| 3 | fit_transform | 260,448 | 7 | 45 | MAINT Param validation for Dictvectorizer (#23820) | {
"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
} | 1 | Python | 8 |
|
0 | 761 | 0877fb0d78635692e481c8bde224fac5ad0dd430 | https://github.com/qutebrowser/qutebrowser.git | 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= | 301 | 84 | qutebrowser | webenginetab.py | qutebrowser/browser/webengine/webenginetab.py | 14 | 117,565 | 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))
| 44 | _on_feature_permission_requested | 321,150 | 54 | 470 | Run scripts/dev/rewrite_enums.py | {
"docstring": "Ask the user for approval for geolocation/media/etc..",
"language": "en",
"n_whitespaces": 6,
"n_words": 7,
"vocab_size": 6
} | 10 | Python | 125 |
|
0 | 784 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | https://github.com/XX-net/XX-Net.git | 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 | 304 | 86 | XX-Net | bdist_msi.py | python3.10.4/Lib/distutils/command/bdist_msi.py | 14 | 56,684 | 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
| 42 | add_find_python | 222,643 | 20 | 469 | add python 3.10.4 for windows | {
"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
} | 3 | Python | 167 |
|
0 | 45 | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | https://github.com/jindongwang/transferlearning.git | def write_exports(self, exports):
rf = self | 32 | 13 | transferlearning | database.py | .venv/lib/python3.8/site-packages/pip/_vendor/distlib/database.py | 11 | 12,781 | def write_exports(self, exports):
rf = self.get_distinfo_file(EXPORTS_FILENAME)
with open(rf, 'w') as f:
write_exports(exports, f)
| 4 | write_exports | 61,961 | 8 | 57 | upd; format | {
"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
} | 1 | Python | 13 |
|
0 | 685 | 621e782ed0c119d2c84124d006fdf253c082449a | https://github.com/ansible/ansible.git | 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 | 264 | 117 | ansible | task_executor.py | lib/ansible/executor/task_executor.py | 15 | 78,856 | 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
| 38 | _get_action_handler_with_module_context | 267,337 | 41 | 420 | 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 | {
"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
} | 8 | Python | 191 |
|
0 | 161 | c17ff17a18f21be60c6916714ac8afd87d4441df | https://github.com/coqui-ai/TTS.git | 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")
| 122 | 40 | TTS | losses.py | TTS/tts/layers/losses.py | 13 | 77,241 | 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
| 12 | forward | 262,500 | 18 | 203 | Fix SSIM loss | {
"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
} | 3 | Python | 61 |
|
0 | 67 | 7346c288e307e1821e3ceb757d686c9bd879389c | https://github.com/django/django.git | 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 | 77 | 22 | django | __init__.py | django/core/management/__init__.py | 13 | 50,200 | 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
| 8 | get_commands | 202,989 | 15 | 126 | 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+. | {
"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
} | 5 | Python | 31 |
|
0 | 193 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | https://github.com/XX-net/XX-Net.git | 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 | 119 | 26 | XX-Net | _parseaddr.py | python3.10.4/Lib/email/_parseaddr.py | 15 | 57,004 | 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
| 14 | getphraselist | 223,611 | 13 | 196 | add python 3.10.4 for windows | {
"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
} | 6 | Python | 35 |
|
0 | 363 | 8387676bc049d7b3e071846730c632e6ced137ed | https://github.com/matplotlib/matplotlib.git | def set_location(self, location):
# This puts the rectangle | 130 | 97 | matplotlib | _secondary_axes.py | lib/matplotlib/axes/_secondary_axes.py | 15 | 23,720 | 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))
| 17 | set_location | 109,724 | 19 | 230 | Clean up code in SecondaryAxis | {
"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
} | 5 | Python | 142 |
|
0 | 149 | e7cb2e82f8b9c7a68f82abdd3b6011d661230b7e | https://github.com/modin-project/modin.git | 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
| 70 | 19 | modin | partition.py | modin/core/execution/ray/implementations/pandas_on_ray/partitioning/partition.py | 14 | 35,383 | 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
| 11 | length | 153,347 | 14 | 115 | REFACTOR-#4251: define public interfaces in `modin.core.execution.ray` module (#3868)
Signed-off-by: Anatoly Myachev <anatoly.myachev@intel.com> | {
"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
} | 4 | Python | 24 |
|
0 | 44 | 0f6dde45a1c75b02c208323574bdb09b8536e3e4 | https://github.com/sympy/sympy.git | def dmp_l2_norm_squared(f, u, K):
if not u:
return dup_l2_norm_squared(f, K)
v = u - 1
return s | 44 | 23 | sympy | densearith.py | sympy/polys/densearith.py | 10 | 47,480 | 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 ])
| 5 | dmp_l2_norm_squared | 195,939 | 8 | 67 | Add `l2_norm_squared` methods. | {
"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
} | 3 | Python | 25 |
|
0 | 72 | a06fa496d3f837cca3c437ab6e9858525633d147 | https://github.com/ansible/ansible.git | 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( | 45 | 32 | ansible | __init__.py | test/lib/ansible_test/_internal/commands/integration/cloud/__init__.py | 9 | 78,551 | 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
| 7 | cloud_filter | 266,740 | 9 | 74 | 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. | {
"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
} | 3 | Python | 40 |
|
0 | 252 | f1c37893caf90738288e789c3233ab934630254f | https://github.com/saltstack/salt.git | def test_upgrade_available_none():
chk_upgrade_out = (
"Last metadata ex | 124 | 56 | salt | test_aixpkg.py | tests/pytests/unit/modules/test_aixpkg.py | 16 | 53,805 | 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
| 21 | test_upgrade_available_none | 215,087 | 19 | 217 | Working tests for install | {
"docstring": "\n test upgrade available where a valid upgrade is not available\n ",
"language": "en",
"n_whitespaces": 17,
"n_words": 10,
"vocab_size": 8
} | 1 | Python | 64 |
|
0 | 373 | 361b7f444a53cc34cad8ddc378d125b7027d96df | https://github.com/getsentry/sentry.git | def test_too_many_boosted_releases_do_not_boost_anymore(self):
release_2 = Release.get_or_create( | 185 | 46 | sentry | test_event_manager.py | tests/sentry/event_manager/test_event_manager.py | 14 | 18,273 | 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
| 27 | test_too_many_boosted_releases_do_not_boost_anymore | 87,293 | 27 | 342 | 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 | {
"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
} | 2 | Python | 56 |
|
0 | 175 | 5dfd57af2a141a013ae3753e160180b82bec9469 | https://github.com/networkx/networkx.git | def hits(G, max_iter=100, tol=1.0e-8, nstart=None, normalized=True):
import numpy as np
import scipy as sp
imp | 226 | 56 | networkx | hits_alg.py | networkx/algorithms/link_analysis/hits_alg.py | 15 | 41,745 | 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
| 20 | hits | 176,175 | 39 | 339 | 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> | {
"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
} | 4 | Python | 90 |
|
0 | 87 | 26e8d6d7664bbaae717438bdb41766550ff57e4f | https://github.com/apache/airflow.git | def test_connection(self) -> Tuple[bool, str]:
try:
conn = se | 41 | 21 | airflow | ftp.py | airflow/providers/ftp/hooks/ftp.py | 11 | 8,731 | 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)
| 8 | test_connection | 45,823 | 10 | 71 | 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> | {
"docstring": "Test the FTP connection by calling path with directory",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | 2 | Python | 22 |
|
0 | 352 | 1661ddd44044c637526e9a1e812e7c1863be35fc | https://github.com/OpenBB-finance/OpenBBTerminal.git | 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 | 131 | 64 | OpenBBTerminal | crypto_controller.py | openbb_terminal/cryptocurrency/crypto_controller.py | 13 | 85,397 | 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")
| 26 | call_price | 285,727 | 28 | 221 | 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 | {
"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
} | 5 | Python | 74 |
|
0 | 316 | 6ed6ac9448311930557810383d2cfd4fe6aae269 | https://github.com/huggingface/datasets.git | 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 | 259 | 107 | datasets | py_utils.py | src/datasets/utils/py_utils.py | 13 | 21,793 | 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)
| 21 | _single_map_nested | 104,238 | 38 | 398 | 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 | {
"docstring": "Apply a function recursively to each element of a nested data struct.",
"language": "en",
"n_whitespaces": 11,
"n_words": 12,
"vocab_size": 11
} | 17 | Python | 182 |
|
0 | 99 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | https://github.com/django/django.git | def test_unified(self):
| 77 | 26 | django | tests.py | tests/admin_scripts/tests.py | 11 | 51,930 | 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")
| 9 | test_unified | 207,334 | 11 | 140 | Refs #33476 -- Reformatted code with Black. | {
"docstring": "--output=unified emits settings diff in unified mode.",
"language": "en",
"n_whitespaces": 6,
"n_words": 7,
"vocab_size": 7
} | 1 | Python | 35 |
|
0 | 18 | eb2692cb32bb1747e312d5b20e976d7a879c9588 | https://github.com/ray-project/ray.git | def runtime_env(self):
| 17 | 4 | ray | runtime_context.py | python/ray/runtime_context.py | 9 | 31,793 | def runtime_env(self):
return RuntimeEnv.deserialize(self._get_runtime_env_string())
| 2 | runtime_env | 139,848 | 5 | 31 | [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. | {
"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
} | 1 | Python | 4 |
|
0 | 65 | d1a8d1597d4fe9f129a72fe94c1508304b7eae0f | https://github.com/streamlink/streamlink.git | def sleeper(self, duration):
s = time()
yield
time_to_sleep = duration - (time() - s)
if time_to_sleep > 0:
s | 36 | 16 | streamlink | dash.py | src/streamlink/stream/dash.py | 11 | 45,770 | def sleeper(self, duration):
s = time()
yield
time_to_sleep = duration - (time() - s)
if time_to_sleep > 0:
self.wait(time_to_sleep)
| 6 | sleeper | 187,407 | 7 | 63 | 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. | {
"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
} | 2 | Python | 19 |
|
0 | 1,100 | a17f4f3bd63e3ca3754f96d7db4ce5197720589b | https://github.com/matplotlib/matplotlib.git | 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 | 1,470 | 192 | matplotlib | test_colors.py | lib/matplotlib/tests/test_colors.py | 12 | 23,566 | 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)))
| 119 | test_BoundaryNorm | 109,399 | 52 | 2,192 | MNT: convert tests and internal usage way from using mpl.cm.get_cmap | {
"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
} | 4 | Python | 623 |
|
0 | 912 | e5b1888cd932909e49194d58035da34b210b91c4 | https://github.com/modin-project/modin.git | 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 | 315 | 113 | modin | dataframe.py | modin/experimental/core/execution/native/implementations/hdk_on_native/dataframe/dataframe.py | 16 | 36,066 | 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
| 57 | _join_by_index | 154,556 | 44 | 498 | 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> | {
"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
} | 11 | Python | 171 |
|
0 | 112 | e272ed2fa4c58e0a89e273a3e85da7d13a85e04c | https://github.com/OpenMined/PySyft.git | 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 | 91 | 22 | PySyft | function_or_constructor_action.py | packages/syft/src/syft/core/node/common/action/function_or_constructor_action.py | 13 | 342 | 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),
)
| 23 | _object2proto | 2,710 | 16 | 135 | [syft.core.node.common.action] Change syft import absolute -> relative | {
"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
} | 3 | Python | 25 |
|
0 | 370 | dec723f072eb997a497a159dbe8674cd39999ee9 | https://github.com/networkx/networkx.git | 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],
[],
],
| 152 | 46 | networkx | small.py | networkx/generators/small.py | 9 | 41,741 | 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
| 34 | truncated_cube_graph | 176,171 | 5 | 197 | 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> | {
"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
} | 1 | Python | 56 |
|
0 | 53 | de3fcba9e95818e9634ab7de6bfcb1f4221f2775 | https://github.com/wagtail/wagtail.git | def get_admin_urls_for_registration(self):
urls = ()
for instance in self.modeladmin_instances:
urls += instance.get_admin_urls_for_registration()
return urls
| 26 | 12 | wagtail | options.py | wagtail/contrib/modeladmin/options.py | 10 | 15,593 | def get_admin_urls_for_registration(self):
urls = ()
for instance in self.modeladmin_instances:
urls += instance.get_admin_urls_for_registration()
return urls
| 5 | get_admin_urls_for_registration | 70,994 | 5 | 45 | Fix warnings from flake8-comprehensions. | {
"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
} | 2 | Python | 14 |
|
0 | 63 | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | https://github.com/jindongwang/transferlearning.git | def setName(self, name):
self.name = name
self.errmsg = "Expected " + self.name
if __diag__.enable_debug_on_named_expressions:
self.setDebug()
return self
| 34 | 15 | transferlearning | pyparsing.py | .venv/lib/python3.8/site-packages/pip/_vendor/pyparsing.py | 9 | 13,241 | def setName(self, name):
self.name = name
self.errmsg = "Expected " + self.name
if __diag__.enable_debug_on_named_expressions:
self.setDebug()
return self
| 6 | setName | 63,304 | 7 | 59 | upd; format | {
"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
} | 2 | Python | 17 |
|
0 | 82 | 1e65a4afd191cf61ba05b80545d23f9b88962f41 | https://github.com/modin-project/modin.git | 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)
| 54 | 16 | modin | groupby.py | modin/core/dataframe/algebra/default2pandas/groupby.py | 12 | 35,257 | 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)
| 7 | get_func | 153,097 | 5 | 92 | FIX-#3197: do not pass lambdas to the backend in GroupBy (#3373)
Signed-off-by: Dmitry Chigarev <dmitry.chigarev@intel.com> | {
"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
} | 3 | Python | 21 |
|
0 | 44 | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | https://github.com/ray-project/ray.git | def update_scheduler(self, metric):
self.worker_group.apply_all_operators(
lambda op: [sched.step(m | 32 | 12 | ray | torch_trainer.py | python/ray/util/sgd/torch/torch_trainer.py | 11 | 29,983 | def update_scheduler(self, metric):
self.worker_group.apply_all_operators(
lambda op: [sched.step(metric) for sched in op._schedulers]
)
| 4 | update_scheduler | 133,351 | 9 | 52 | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | {
"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
} | 2 | Python | 12 |
|
0 | 56 | a5b70b3132467b5e3616178d9ecca6cb7316c400 | https://github.com/scikit-learn/scikit-learn.git | def paired_cosine_distances(X, Y):
X, Y = c | 39 | 27 | scikit-learn | pairwise.py | sklearn/metrics/pairwise.py | 11 | 75,273 | 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,
}
| 3 | paired_cosine_distances | 258,521 | 10 | 108 | DOC Ensures that sklearn.metrics.pairwise.paired_cosine_distances passes numpydoc validation (#22141)
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> | {
"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
} | 1 | Python | 31 |
|
0 | 131 | 897a8dd89f40817201bc4aebe532a096405bdeb1 | https://github.com/huggingface/transformers.git | def torchdynamo_smart_context_manager(self):
ctx_manager = contextlib.nullcontext()
if is_torchdynamo_available():
import torchdynamo
from torchdy | 64 | 20 | transformers | trainer.py | src/transformers/trainer.py | 13 | 5,648 | 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
| 10 | torchdynamo_smart_context_manager | 30,695 | 12 | 112 | 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> | {
"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
} | 4 | Python | 29 |
|
0 | 110 | 7d9e9a49005de7961e84d2a7c608db57dbab3046 | https://github.com/certbot/certbot.git | 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
| 53 | 17 | certbot | parser.py | certbot-apache/certbot_apache/_internal/parser.py | 11 | 45,584 | 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
| 13 | check_aug_version | 186,677 | 9 | 98 | Add typing to certbot.apache (#9071)
* Add typing to certbot.apache
Co-authored-by: Adrien Ferrand <ferrand.ad@gmail.com> | {
"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
} | 2 | Python | 20 |
|
0 | 42 | ca86da3a30c4e080d4db8c25fca73de843663cb4 | https://github.com/Stability-AI/stablediffusion.git | 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
| 58 | 17 | stablediffusion | utils.py | ldm/modules/midas/utils.py | 12 | 37,003 | 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
| 6 | resize_depth | 157,635 | 13 | 91 | release more models | {
"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
} | 1 | Python | 20 |
|
0 | 729 | cda8dfe6f45dc5ed394c2f5cda706cd6c729f713 | https://github.com/sympy/sympy.git | def comp(z1, z2, tol=None):
r
if type(z2) is str:
if not | 381 | 107 | sympy | numbers.py | sympy/core/numbers.py | 24 | 47,440 | 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
| 105 | comp | 195,853 | 34 | 605 | Improved documentation formatting | {
"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
} | 26 | Python | 213 |
|
0 | 491 | 36c1f477b273cb2fb0dea3c921ec267db877c039 | https://github.com/open-mmlab/mmdetection.git | 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 | 122 | 45 | mmdetection | openimages.py | mmdet/datasets/openimages.py | 19 | 70,677 | 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
| 23 | _parse_img_level_ann | 245,152 | 24 | 201 | Refactor OpenImages. | {
"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
} | 3 | Python | 58 |
|
0 | 32 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | https://github.com/XX-net/XX-Net.git | def logical_and(self, a, b):
a = _convert | 31 | 11 | XX-Net | _pydecimal.py | python3.10.4/Lib/_pydecimal.py | 9 | 55,789 | def logical_and(self, a, b):
a = _convert_other(a, raiseit=True)
return a.logical_and(b, context=self)
| 3 | logical_and | 219,771 | 7 | 48 | add python 3.10.4 for windows | {
"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
} | 1 | Python | 11 |
|
0 | 587 | 65be461082dda54c8748922f9c29a19af1279fe1 | https://github.com/sympy/sympy.git | 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 | 214 | 114 | sympy | enumerative.py | sympy/utilities/enumerative.py | 18 | 48,514 | 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
| 21 | decrement_part_small | 197,371 | 16 | 333 | Remove abbreviations in documentation | {
"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
} | 13 | Python | 182 |
|
0 | 40 | 90cea203befa8f2e86e9c1c18bb3972296358e7b | https://github.com/ray-project/ray.git | def get_node_id(self) -> str:
node_id = self.worker.current_node_id
assert not node_id.is_nil()
return node_i | 28 | 12 | ray | runtime_context.py | python/ray/runtime_context.py | 8 | 27,935 | def get_node_id(self) -> str:
node_id = self.worker.current_node_id
assert not node_id.is_nil()
return node_id.hex()
| 12 | get_node_id | 125,638 | 8 | 49 | 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 | {
"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
} | 1 | Python | 12 |
|
0 | 57 | 6c4e2810285af0698538aed9d46a99de085eb310 | https://github.com/qutebrowser/qutebrowser.git | def list_option(*, info):
return _option(
info,
"List options",
lambda opt: (isinstance(info.config.get_obj(op | 41 | 16 | qutebrowser | configmodel.py | qutebrowser/completion/models/configmodel.py | 15 | 117,392 | def list_option(*, info):
return _option(
info,
"List options",
lambda opt: (isinstance(info.config.get_obj(opt.name), list) and
not opt.no_autoconfig)
)
| 7 | list_option | 320,849 | 10 | 67 | pylint: Fix new unnecessary-lambda-assignment | {
"docstring": "A CompletionModel filled with settings whose values are lists.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | 2 | Python | 16 |
|
1 | 201 | 2e7ee756eb1d55080d707cef63454788a7abb6be | https://github.com/airbytehq/airbyte.git | 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, | 73 | 44 | airbyte | test_source.py | airbyte-integrations/connectors/source-google-ads/unit_tests/test_source.py | 12 | @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 | 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"],
),
(, []),
],
) | 14 | get_instance_from_config_with_end_date | 3,546 | 18 | 208 | 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> | {
"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
} | 1 | Python | 53 |
0 | 144 | 34d9d630bb02426d297d3e20fedb7da8c3ced03a | https://github.com/networkx/networkx.git | 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 | 132 | 49 | networkx | pairs.py | networkx/algorithms/assortativity/pairs.py | 12 | 41,838 | 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
| 12 | node_degree_xy | 176,324 | 21 | 209 | 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> | {
"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
} | 7 | Python | 69 |
|
0 | 88 | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | https://github.com/ray-project/ray.git | def validate(self, num_steps=None, profile=False, reduce_results=True, info=None):
worker_stats = self.worker_group.validate(
| 56 | 18 | ray | torch_trainer.py | python/ray/util/sgd/torch/torch_trainer.py | 9 | 29,985 | 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
| 8 | validate | 133,353 | 9 | 85 | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | {
"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
} | 2 | Python | 20 |
|
0 | 29 | cc4d0564756ca067516f71718a3d135996525909 | https://github.com/jindongwang/transferlearning.git | def set_raw_scale(self, in_, scale):
self.__check_input(in_)
self.raw_scale[in_] = scale
| 24 | 8 | transferlearning | io.py | code/deep/BJMMD/caffe/python/caffe/io.py | 8 | 12,047 | def set_raw_scale(self, in_, scale):
self.__check_input(in_)
self.raw_scale[in_] = scale
| 3 | set_raw_scale | 60,255 | 6 | 39 | Balanced joint maximum mean discrepancy for deep transfer learning | {
"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
} | 1 | Python | 8 |
|
0 | 238 | d1aa5608979891e3dd859c07fa919fa01cfead5f | https://github.com/ray-project/ray.git | 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])
| 291 | 61 | ray | test_bazel_sharding.py | ci/run/bazel_sharding/tests/test_bazel_sharding.py | 10 | 30,178 | 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])
| 25 | test_add_rule_to_best_shard | 134,046 | 16 | 460 | [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> | {
"docstring": "Test that the best shard in optimal strategy is chosen correctly.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | 7 | Python | 151 |
|
0 | 37 | 6c38a6b5697bcf4587e00101771001bf596974f9 | https://github.com/home-assistant/core.git | def async_heartbeat(self) -> None:
self._computed_state = False
self._restart_timer()
self.async_write_ha_stat | 23 | 9 | core | binary_sensor.py | homeassistant/components/isy994/binary_sensor.py | 7 | 110,798 | def async_heartbeat(self) -> None:
self._computed_state = False
self._restart_timer()
self.async_write_ha_state()
| 11 | async_heartbeat | 312,146 | 5 | 42 | Enable strict typing for isy994 (#65439)
Co-authored-by: Martin Hjelmare <marhje52@gmail.com> | {
"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
} | 1 | Python | 9 |
|
0 | 49 | 7e23a37e1c5bda81234801a6584563e2880769eb | https://github.com/pandas-dev/pandas.git | 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)
| 72 | 20 | pandas | test_assert_series_equal.py | pandas/tests/util/test_assert_series_equal.py | 12 | 39,861 | 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)
| 11 | test_assert_series_equal_interval_dtype_mismatch | 166,848 | 17 | 123 | ENH: consistency of input args for boundaries - Interval (#46522) | {
"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
} | 1 | Python | 24 |
|
0 | 85 | 7c6c5f6215b40a27cfefb7bf21246299fd9b3a1e | https://github.com/matplotlib/matplotlib.git | def rc_file_defaults():
# | 41 | 32 | matplotlib | __init__.py | lib/matplotlib/__init__.py | 12 | 23,106 | 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})
| 5 | rc_file_defaults | 108,225 | 10 | 72 | Fix removed cross-references | {
"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
} | 3 | Python | 35 |
|
0 | 153 | f3166e673fe8d40277b804d35d77dcdb760fc3b3 | https://github.com/pypa/pipenv.git | 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:
| 54 | 19 | pipenv | syntax.py | pipenv/patched/notpip/_vendor/rich/syntax.py | 11 | 3,587 | 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
| 16 | lexer | 20,845 | 12 | 83 | 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 | {
"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
} | 3 | Python | 21 |
|
0 | 19 | a35b29b2651bf33c5d5b45e64bc7765ffde4aff4 | https://github.com/saltstack/salt.git | def test_numeric_repl(file, multiline_file):
file.replace(multiline_fi | 27 | 10 | salt | test_replace.py | tests/pytests/functional/modules/file/test_replace.py | 8 | 54,182 | def test_numeric_repl(file, multiline_file):
file.replace(multiline_file, r"Etiam", 123)
assert "123" in multiline_file.read_text()
| 3 | test_numeric_repl | 215,808 | 5 | 46 | 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 | {
"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
} | 1 | Python | 10 |
|
0 | 29 | 7fa8e45b6782d545fa0ead112d92d13bdad7417c | https://github.com/gradio-app/gradio.git | def set_interpret_parameters(self, segments=16):
self.interpretation_segments = segments
retu | 17 | 8 | gradio | components.py | gradio/components.py | 7 | 43,005 | def set_interpret_parameters(self, segments=16):
self.interpretation_segments = segments
return self
| 3 | set_interpret_parameters | 179,715 | 4 | 29 | Blocks-Components
- fixes
- format | {
"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
} | 1 | Python | 8 |
|
0 | 24 | 1fe202a1a3343fad77da270ffe0923a46f1944dd | https://github.com/matrix-org/synapse.git | def can_native_upsert(self) -> bool:
return sqlite3.sqlite_version_info >= (3, 2 | 20 | 10 | synapse | sqlite.py | synapse/storage/engines/sqlite.py | 7 | 72,207 | def can_native_upsert(self) -> bool:
return sqlite3.sqlite_version_info >= (3, 24, 0)
| 6 | can_native_upsert | 248,309 | 5 | 32 | Tidy up and type-hint the database engine modules (#12734)
Co-authored-by: Sean Quah <8349537+squahtx@users.noreply.github.com> | {
"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
} | 1 | Python | 10 |
|
0 | 97 | 30ab5458a7e4ba2351d5e1beef8c8797b5946493 | https://github.com/ray-project/ray.git | async def get_actors(self) -> dict:
reply = await self._client.get_all_actor_info(timeout=DEFAULT_RPC_TIMEOUT)
result = {}
for message in rep | 67 | 22 | ray | state_aggregator.py | dashboard/state_aggregator.py | 13 | 31,405 | 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
| 14 | get_actors | 138,397 | 16 | 111 | [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. | {
"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
} | 2 | Python | 29 |
|
0 | 833 | 551205a18ac2ac19626f4e4ffb2ed88fcad705b9 | https://github.com/mindsdb/mindsdb.git | 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 | 445 | 109 | mindsdb | mysql_proxy.py | mindsdb/api/mysql/mysql_proxy/mysql_proxy.py | 16 | 25,051 | 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()
| 63 | insert_predictor_answer | 113,876 | 42 | 713 | fix | {
"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
} | 18 | Python | 181 |
|
0 | 469 | 2c3e10a128fa0ce4e937d8d50dc0cd6d7cd11485 | https://github.com/OpenBB-finance/OpenBBTerminal.git | def populate_historical_trade_data(self):
trade_data = self.__orderbook.pivot(
index="Date",
columns="Ticker",
values=[
"Type",
"Sector",
"Industry",
"Country",
"Price",
"Quantity",
| 164 | 65 | OpenBBTerminal | portfolio_model.py | openbb_terminal/portfolio/portfolio_model.py | 12 | 85,115 | 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
| 34 | populate_historical_trade_data | 285,032 | 23 | 282 | 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> | {
"docstring": "Create a new dataframe to store historical prices by ticker",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | 1 | Python | 78 |
|
1 | 129 | a47d569e670fd4102af37c3165c9b1ddf6fd3005 | https://github.com/scikit-learn/scikit-learn.git | 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"]) | 89 | 39 | scikit-learn | test_openml.py | sklearn/datasets/tests/test_openml.py | 9 | @fails_if_pypy
@pytest.mark.parametrize("parser", ["liac-arff", "pandas"]) | 75,979 | 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"]) | 18 | test_fetch_openml_equivalence_array_dataframe | 259,898 | 20 | 167 | 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> | {
"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
} | 1 | Python | 47 |
0 | 76 | 002f919dda5f01d067c2e786426c68751551d15c | https://github.com/mitmproxy/mitmproxy.git | def wire_type(self):
if hasattr(self, '_m_wire_type'):
return self._m_wire_type
self._m_wire_type = Kaita | 51 | 15 | mitmproxy | google_protobuf.py | mitmproxy/contrib/kaitaistruct/google_protobuf.py | 12 | 73,944 | 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)
| 5 | wire_type | 252,396 | 12 | 83 | update kaitai definitions | {
"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
} | 2 | Python | 17 |
|
0 | 80 | b3587b52b25077f68116b9852b041d33e7fc6601 | https://github.com/mitmproxy/mitmproxy.git | def address(self): # pragma: no cover
warnings.warn(
"Client.address is deprecated, use Client.peername instead.",
D | 23 | 18 | mitmproxy | connection.py | mitmproxy/connection.py | 8 | 73,687 | def address(self): # pragma: no cover
warnings.warn(
"Client.address is deprecated, use Client.peername instead.",
DeprecationWarning,
stacklevel=2,
)
return self.peername
| 7 | address | 251,333 | 7 | 40 | make it black! | {
"docstring": "*Deprecated:* An outdated alias for Client.peername.",
"language": "en",
"n_whitespaces": 5,
"n_words": 6,
"vocab_size": 6
} | 1 | Python | 18 |
|
3 | 80 | a4fdabab38def4bf6b4007f8cd67d6944740b303 | https://github.com/sympy/sympy.git | def jordan_block(kls, size=None, eigenvalue=None, *, band='upper', **kwargs):
if 'r | 239 | 19 | sympy | common.py | sympy/matrices/common.py | 12 | f"""\
To get asquare Jordan block matrix use a morebanded matrix | 48,242 | 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 | 45 | jordan_block | 196,907 | 21 | 109 | Update the Matrix.jordan_block() rows and cols kwargs deprecation | {
"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
} | 16 | Python | 28 |
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