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semeru

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code-code-galeras-code-completion-from-docstring-3k-deduped

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Semeru Lab 7

f65417656ba8c59438d832b6e2a431f78d40c21c

https://github.com/pandas-dev/pandas.git

def rolling(self, *args, **kwargs) -> RollingGroupby: from pandas.core.window import RollingGroupby

pandas

groupby.py

pandas/core/groupby/groupby.py

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, )

rolling

TYP: more return annotations in core/ (#47618) * TYP: more return annotations in core/ * from __future__ import annotations * more __future__

Python

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

networkx

degree_seq.py

networkx/generators/degree_seq.py

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

expected_degree_graph

Remove redundant py2 numeric conversions (#5661) * Remove redundant float conversion * Remove redundant int conversion * Use integer division Co-authored-by: Miroslav Šedivý <[email protected]>

Python

4c58179509e6f6047789efb0a95c2b0e20cb6c8f

https://github.com/mlflow/mlflow.git

def save(self, path): os.makedirs(path,

mlflow

base.py

mlflow/models/evaluation/base.py

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))

save

Improve evaluation api (#5256) * init Signed-off-by: Weichen Xu <[email protected]> * update Signed-off-by: Weichen Xu <[email protected]> * update Signed-off-by: Weichen Xu <[email protected]> * update doc Signed-off-by: Weichen Xu <[email protected]> * update doc Signed-off-by: Weichen Xu <[email protected]> * address comments Signed-off-by: Weichen Xu <[email protected]> * update doc Signed-off-by: Weichen Xu <[email protected]> * add shap limitation on value type Signed-off-by: Weichen Xu <[email protected]> * fix format Signed-off-by: Weichen Xu <[email protected]> * update Signed-off-by: Weichen Xu <[email protected]> * update Signed-off-by: Weichen Xu <[email protected]> * update Signed-off-by: Weichen Xu <[email protected]> * update Signed-off-by: Weichen Xu <[email protected]> * update Signed-off-by: Weichen Xu <[email protected]>

Python

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"] =

sentry

test_message_builder.py

tests/sentry/integrations/slack/test_message_builder.py

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

test_build_group_generic_issue_attachment

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

Python

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:

DeepFaceLive

ImageProcessor.py

xlib/image/ImageProcessor.py

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

apply

ImageProcessor.py refactoring

Python

843dba903757d592f7703a83ebd75eb3ffb46f6f

https://github.com/microsoft/recommenders.git

def predict(self, x): # start the timer self.timer.start() v_, _ = self

recommenders

rbm.py

recommenders/models/rbm/rbm.py

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

predict

removed time from returning args

Python

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

XX-Net

decoder.py

python3.10.4/Lib/json/decoder.py

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

raw_decode

add python 3.10.4 for windows

Python

aa1f40a93a882db304e9a06c2a11d93b2532d80a

https://github.com/networkx/networkx.git

def has_bridges(G, root=None): try: next(bridges

networkx

bridges.py

networkx/algorithms/bridges.py

@not_implemented_for("multigraph") @not_implemented_for("directed")

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")

has_bridges

Improve bridges documentation (#5519) * Fix bridges documentation * Revert source code modification * Revert raise errors for multigraphs

Python

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

transferlearning

wheel.py

.venv/lib/python3.8/site-packages/pip/_internal/utils/wheel.py

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)

wheel_metadata

upd; format

Python

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

datasets

table.py

src/datasets/table.py

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)

remove_column

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 <[email protected]> * Update index mdx * Update docs/source/access.mdx Co-authored-by: Steven Liu <[email protected]> * 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 <[email protected]> Co-authored-by: Mishig Davaadorj <[email protected]> Co-authored-by: Lysandre Debut <[email protected]> Co-authored-by: Mishig Davaadorj <[email protected]> * 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 <[email protected]> * 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 <[email protected]> Co-authored-by: Quentin Lhoest <[email protected]> Co-authored-by: Quentin Lhoest <[email protected]> Co-authored-by: Lysandre Debut <[email protected]>

Python

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()

netbox

test_models.py

netbox/dcim/tests/test_models.py

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()

test_cable_cannot_terminate_to_a_wireless_interface

Update Cable instantiations to match new signature

Python

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

django

creation.py

django/db/backends/base/creation.py

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), }

get_test_db_clone_settings

Refs #33476 -- Reformatted code with Black.

Python

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')

XX-Net

imaplib.py

python3.10.4/Lib/imaplib.py

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')

open

add python 3.10.4 for windows

Python

7f27e70440c177b2a047b7f74a78ed5cd5b4b596

https://github.com/Textualize/textual.git

def synchronized_output_end_sequence(self) -> str: if self.synchronised_output: return

textual

_terminal_features.py

src/textual/_terminal_features.py

def synchronized_output_end_sequence(self) -> str: if self.synchronised_output: return TERMINAL_MODES_ANSI_SEQUENCES[Mode.SynchronizedOutput]["end_sync"] return ""

synchronized_output_end_sequence

[terminal buffering] Address PR feedback

Python

f6021faf2a8e62f88a8d6979ce812dcb71133a8f

https://github.com/jaakkopasanen/AutoEq.git

def _band_penalty_coefficients(self, fc, q, gain, filter_frs): ref_frs = biquad.digital_coeffs(self.frequenc

AutoEq

frequency_response.py

frequency_response.py

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))

_band_penalty_coefficients

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.

Python

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

scikit-learn

test_voting.py

sklearn/ensemble/tests/test_voting.py

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])

test_predict_on_toy_problem

TST use global_random_seed in sklearn/ensemble/tests/test_voting.py (#24282) Co-authored-by: Jérémie du Boisberranger <[email protected]>

Python

5a850eb044ca07f1f3bcb1b284116d6f2d37df1b

https://github.com/scikit-learn/scikit-learn.git

def fit_transform(self, X, y=None): self._validate_params() return self._tran

scikit-learn

_dict_vectorizer.py

sklearn/feature_extraction/_dict_vectorizer.py

def fit_transform(self, X, y=None): self._validate_params() return self._transform(X, fitting=True)

fit_transform

MAINT Param validation for Dictvectorizer (#23820)

Python

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=

qutebrowser

webenginetab.py

qutebrowser/browser/webengine/webenginetab.py

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))

_on_feature_permission_requested

Run scripts/dev/rewrite_enums.py

Python

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

XX-Net

bdist_msi.py

python3.10.4/Lib/distutils/command/bdist_msi.py

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

add_find_python

add python 3.10.4 for windows

Python

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

https://github.com/jindongwang/transferlearning.git

def write_exports(self, exports): rf = self

transferlearning

database.py

.venv/lib/python3.8/site-packages/pip/_vendor/distlib/database.py

def write_exports(self, exports): rf = self.get_distinfo_file(EXPORTS_FILENAME) with open(rf, 'w') as f: write_exports(exports, f)

write_exports

upd; format

Python

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

ansible

task_executor.py

lib/ansible/executor/task_executor.py

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

_get_action_handler_with_module_context

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

Python

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")

TTS

losses.py

TTS/tts/layers/losses.py

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

forward

Fix SSIM loss

Python

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

django

__init__.py

django/core/management/__init__.py

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

get_commands

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+.

Python

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

XX-Net

_parseaddr.py

python3.10.4/Lib/email/_parseaddr.py

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

getphraselist

add python 3.10.4 for windows

Python

8387676bc049d7b3e071846730c632e6ced137ed

https://github.com/matplotlib/matplotlib.git

def set_location(self, location): # This puts the rectangle

matplotlib

_secondary_axes.py

lib/matplotlib/axes/_secondary_axes.py

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))

set_location

Clean up code in SecondaryAxis

Python

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

modin

partition.py

modin/core/execution/ray/implementations/pandas_on_ray/partitioning/partition.py

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

length

REFACTOR-#4251: define public interfaces in `modin.core.execution.ray` module (#3868) Signed-off-by: Anatoly Myachev <[email protected]>

Python

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

sympy

densearith.py

sympy/polys/densearith.py

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 ])

dmp_l2_norm_squared

Add `l2_norm_squared` methods.

Python

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(

ansible

__init__.py

test/lib/ansible_test/_internal/commands/integration/cloud/__init__.py

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

cloud_filter

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.

Python

f1c37893caf90738288e789c3233ab934630254f

https://github.com/saltstack/salt.git

def test_upgrade_available_none(): chk_upgrade_out = ( "Last metadata ex

salt

test_aixpkg.py

tests/pytests/unit/modules/test_aixpkg.py

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

test_upgrade_available_none

Working tests for install

Python

361b7f444a53cc34cad8ddc378d125b7027d96df

https://github.com/getsentry/sentry.git

def test_too_many_boosted_releases_do_not_boost_anymore(self): release_2 = Release.get_or_create(

sentry

test_event_manager.py

tests/sentry/event_manager/test_event_manager.py

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

test_too_many_boosted_releases_do_not_boost_anymore

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

Python

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

networkx

hits_alg.py

networkx/algorithms/link_analysis/hits_alg.py

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

hits

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 <[email protected]> Co-authored-by: Dan Schult <[email protected]> * 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 <[email protected]> * 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 <[email protected]> * 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 <[email protected]> * 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 <[email protected]> Co-authored-by: Jarrod Millman <[email protected]> Co-authored-by: Andrew Knyazev <[email protected]> Co-authored-by: Dan Schult <[email protected]> Co-authored-by: eskountis <[email protected]> Co-authored-by: Anutosh Bhat <[email protected]> Co-authored-by: NikHoh <[email protected]> Co-authored-by: Hohmann, Nikolas <[email protected]> Co-authored-by: Sultan Orazbayev <[email protected]> Co-authored-by: Mridul Seth <[email protected]>

Python

26e8d6d7664bbaae717438bdb41766550ff57e4f

https://github.com/apache/airflow.git

def test_connection(self) -> Tuple[bool, str]: try: conn = se

airflow

ftp.py

airflow/providers/ftp/hooks/ftp.py

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)

test_connection

Updates FTPHook provider to have test_connection (#21997) * Updates FTP provider to have test_connection Co-authored-by: eladkal <[email protected]>

Python

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

OpenBBTerminal

crypto_controller.py

openbb_terminal/cryptocurrency/crypto_controller.py

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")

call_price

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 <[email protected]>; COlin

Python

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

datasets

py_utils.py

src/datasets/utils/py_utils.py

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)

_single_map_nested

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

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

https://github.com/django/django.git

def test_unified(self):

django

tests.py

tests/admin_scripts/tests.py

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")

test_unified

Refs #33476 -- Reformatted code with Black.

Python

eb2692cb32bb1747e312d5b20e976d7a879c9588

https://github.com/ray-project/ray.git

def runtime_env(self):

ray

runtime_context.py

python/ray/runtime_context.py

def runtime_env(self): return RuntimeEnv.deserialize(self._get_runtime_env_string())

runtime_env

[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.

Python

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

streamlink

dash.py

src/streamlink/stream/dash.py

def sleeper(self, duration): s = time() yield time_to_sleep = duration - (time() - s) if time_to_sleep > 0: self.wait(time_to_sleep)

sleeper

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.

Python

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

matplotlib

test_colors.py

lib/matplotlib/tests/test_colors.py

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)))

test_BoundaryNorm

MNT: convert tests and internal usage way from using mpl.cm.get_cmap

Python

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

modin

dataframe.py

modin/experimental/core/execution/native/implementations/hdk_on_native/dataframe/dataframe.py

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

_join_by_index

FEAT-#4946: Replace OmniSci with HDK (#4947) Co-authored-by: Iaroslav Igoshev <[email protected]> Signed-off-by: Andrey Pavlenko <[email protected]>

Python

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

PySyft

function_or_constructor_action.py

packages/syft/src/syft/core/node/common/action/function_or_constructor_action.py

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), )

_object2proto

[syft.core.node.common.action] Change syft import absolute -> relative

Python

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], [], ],

networkx

small.py

networkx/generators/small.py

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

truncated_cube_graph

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 <[email protected]>

Python

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

wagtail

options.py

wagtail/contrib/modeladmin/options.py

def get_admin_urls_for_registration(self): urls = () for instance in self.modeladmin_instances: urls += instance.get_admin_urls_for_registration() return urls

get_admin_urls_for_registration

Fix warnings from flake8-comprehensions.

Python

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

transferlearning

pyparsing.py

.venv/lib/python3.8/site-packages/pip/_vendor/pyparsing.py

def setName(self, name): self.name = name self.errmsg = "Expected " + self.name if __diag__.enable_debug_on_named_expressions: self.setDebug() return self

setName

upd; format

Python

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)

modin

groupby.py

modin/core/dataframe/algebra/default2pandas/groupby.py

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)

get_func

FIX-#3197: do not pass lambdas to the backend in GroupBy (#3373) Signed-off-by: Dmitry Chigarev <[email protected]>

Python

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

https://github.com/ray-project/ray.git

def update_scheduler(self, metric): self.worker_group.apply_all_operators( lambda op: [sched.step(m

ray

torch_trainer.py

python/ray/util/sgd/torch/torch_trainer.py

def update_scheduler(self, metric): self.worker_group.apply_all_operators( lambda op: [sched.step(metric) for sched in op._schedulers] )

update_scheduler

[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.

Python

a5b70b3132467b5e3616178d9ecca6cb7316c400

https://github.com/scikit-learn/scikit-learn.git

def paired_cosine_distances(X, Y): X, Y = c

scikit-learn

pairwise.py

sklearn/metrics/pairwise.py

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, }

paired_cosine_distances

DOC Ensures that sklearn.metrics.pairwise.paired_cosine_distances passes numpydoc validation (#22141) Co-authored-by: Thomas J. Fan <[email protected]>

Python

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

transformers

trainer.py

src/transformers/trainer.py

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

torchdynamo_smart_context_manager

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 <[email protected]>

Python

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

certbot

parser.py

certbot-apache/certbot_apache/_internal/parser.py

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

check_aug_version

Add typing to certbot.apache (#9071) * Add typing to certbot.apache Co-authored-by: Adrien Ferrand <[email protected]>

Python

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

stablediffusion

utils.py

ldm/modules/midas/utils.py

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

resize_depth

release more models

Python

cda8dfe6f45dc5ed394c2f5cda706cd6c729f713

https://github.com/sympy/sympy.git

def comp(z1, z2, tol=None): r if type(z2) is str: if not

sympy

numbers.py

sympy/core/numbers.py

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

comp

Improved documentation formatting

Python

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

mmdetection

openimages.py

mmdet/datasets/openimages.py

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

_parse_img_level_ann

Refactor OpenImages.

Python

8198943edd73a363c266633e1aa5b2a9e9c9f526

https://github.com/XX-net/XX-Net.git

def logical_and(self, a, b): a = _convert

XX-Net

_pydecimal.py

python3.10.4/Lib/_pydecimal.py

def logical_and(self, a, b): a = _convert_other(a, raiseit=True) return a.logical_and(b, context=self)

logical_and

add python 3.10.4 for windows

Python

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

sympy

enumerative.py

sympy/utilities/enumerative.py

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

decrement_part_small

Remove abbreviations in documentation

Python

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

ray

runtime_context.py

python/ray/runtime_context.py

def get_node_id(self) -> str: node_id = self.worker.current_node_id assert not node_id.is_nil() return node_id.hex()

get_node_id

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

Python

6c4e2810285af0698538aed9d46a99de085eb310

https://github.com/qutebrowser/qutebrowser.git

def list_option(*, info): return _option( info, "List options", lambda opt: (isinstance(info.config.get_obj(op

qutebrowser

configmodel.py

qutebrowser/completion/models/configmodel.py

def list_option(*, info): return _option( info, "List options", lambda opt: (isinstance(info.config.get_obj(opt.name), list) and not opt.no_autoconfig) )

list_option

pylint: Fix new unnecessary-lambda-assignment

Python

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,

airbyte

test_source.py

airbyte-integrations/connectors/source-google-ads/unit_tests/test_source.py

@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""", []), ], )

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"], ), (, []), ], )

get_instance_from_config_with_end_date

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 <[email protected]>

Python

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

networkx

pairs.py

networkx/algorithms/assortativity/pairs.py

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

node_degree_xy

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 <[email protected]>

Python

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(

ray

torch_trainer.py

python/ray/util/sgd/torch/torch_trainer.py

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

validate

[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.

Python

cc4d0564756ca067516f71718a3d135996525909

https://github.com/jindongwang/transferlearning.git

def set_raw_scale(self, in_, scale): self.__check_input(in_) self.raw_scale[in_] = scale

transferlearning

io.py

code/deep/BJMMD/caffe/python/caffe/io.py

def set_raw_scale(self, in_, scale): self.__check_input(in_) self.raw_scale[in_] = scale

set_raw_scale

Balanced joint maximum mean discrepancy for deep transfer learning

Python

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])

ray

test_bazel_sharding.py

ci/run/bazel_sharding/tests/test_bazel_sharding.py

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])

test_add_rule_to_best_shard

[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 <[email protected]>

Python

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

core

binary_sensor.py

homeassistant/components/isy994/binary_sensor.py

def async_heartbeat(self) -> None: self._computed_state = False self._restart_timer() self.async_write_ha_state()

async_heartbeat

Enable strict typing for isy994 (#65439) Co-authored-by: Martin Hjelmare <[email protected]>

Python

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)

pandas

test_assert_series_equal.py

pandas/tests/util/test_assert_series_equal.py

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)

test_assert_series_equal_interval_dtype_mismatch

ENH: consistency of input args for boundaries - Interval (#46522)

Python

7c6c5f6215b40a27cfefb7bf21246299fd9b3a1e

https://github.com/matplotlib/matplotlib.git

def rc_file_defaults(): #

matplotlib

__init__.py

lib/matplotlib/__init__.py

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})

rc_file_defaults

Fix removed cross-references

Python

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:

pipenv

syntax.py

pipenv/patched/notpip/_vendor/rich/syntax.py

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

lexer

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

Python

a35b29b2651bf33c5d5b45e64bc7765ffde4aff4

https://github.com/saltstack/salt.git

def test_numeric_repl(file, multiline_file): file.replace(multiline_fi

salt

test_replace.py

tests/pytests/functional/modules/file/test_replace.py

def test_numeric_repl(file, multiline_file): file.replace(multiline_file, r"Etiam", 123) assert "123" in multiline_file.read_text()

test_numeric_repl

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

Python

7fa8e45b6782d545fa0ead112d92d13bdad7417c

https://github.com/gradio-app/gradio.git

def set_interpret_parameters(self, segments=16): self.interpretation_segments = segments retu

gradio

components.py

gradio/components.py

def set_interpret_parameters(self, segments=16): self.interpretation_segments = segments return self

set_interpret_parameters

Blocks-Components - fixes - format

Python

1fe202a1a3343fad77da270ffe0923a46f1944dd

https://github.com/matrix-org/synapse.git

def can_native_upsert(self) -> bool: return sqlite3.sqlite_version_info >= (3, 2

synapse

sqlite.py

synapse/storage/engines/sqlite.py

def can_native_upsert(self) -> bool: return sqlite3.sqlite_version_info >= (3, 24, 0)

can_native_upsert

Tidy up and type-hint the database engine modules (#12734) Co-authored-by: Sean Quah <[email protected]>

Python

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

ray

state_aggregator.py

dashboard/state_aggregator.py

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

get_actors

[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.

Python

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

mindsdb

mysql_proxy.py

mindsdb/api/mysql/mysql_proxy/mysql_proxy.py

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()

insert_predictor_answer

fix

Python

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",

OpenBBTerminal

portfolio_model.py

openbb_terminal/portfolio/portfolio_model.py

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

populate_historical_trade_data

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 <[email protected]>

Python

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"])

scikit-learn

test_openml.py

sklearn/datasets/tests/test_openml.py

@fails_if_pypy @pytest.mark.parametrize("parser", ["liac-arff", "pandas"])

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"])

test_fetch_openml_equivalence_array_dataframe

ENH improve ARFF parser using pandas (#21938) Co-authored-by: Thomas J. Fan <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: Adrin Jalali <[email protected]>

Python

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

mitmproxy

google_protobuf.py

mitmproxy/contrib/kaitaistruct/google_protobuf.py

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)

wire_type

update kaitai definitions

Python

b3587b52b25077f68116b9852b041d33e7fc6601

https://github.com/mitmproxy/mitmproxy.git

def address(self): # pragma: no cover warnings.warn( "Client.address is deprecated, use Client.peername instead.", D

mitmproxy

connection.py

mitmproxy/connection.py

def address(self): # pragma: no cover warnings.warn( "Client.address is deprecated, use Client.peername instead.", DeprecationWarning, stacklevel=2, ) return self.peername

address

make it black!

Python

a4fdabab38def4bf6b4007f8cd67d6944740b303

https://github.com/sympy/sympy.git

def jordan_block(kls, size=None, eigenvalue=None, *, band='upper', **kwargs): if 'r

sympy

common.py

sympy/matrices/common.py

f"""\ To get asquare Jordan block matrix use a morebanded matrix

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

jordan_block

Update the Matrix.jordan_block() rows and cols kwargs deprecation

Python