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semeru

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text-code-galeras-code-generation-from-docstring-3k-deduped

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Semeru Lab 7

def tickangle(self): return self["tickangle"]

packages/python/plotly/plotly/graph_objs/bar/marker/_colorbar.py

plotly.py

Python

43e3a4011080911901176aab919c0ecf5046ddd3

_colorbar.py

tickangle

https://github.com/plotly/plotly.py.git

switch to black .22

def _reset_replica_iterator(self): replicas = list(self.in_flight_queries.keys()) random.shuffle(replicas) self.replica_iterator = itertools.cycle(replicas)

python/ray/serve/_private/router.py

ray

Python

545c51609f0f55b41cf99cec95a9c21bee6846de

router.py

_reset_replica_iterator

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

[Serve] ServeHandle detects ActorError and drop replicas from target group (#26685)

def test_workflow_job_template_copy(workflow_job_template, post, get, admin, organization): workflow_job_template.organization = organization label = Label.objects.create(name="foobar", organization=organization) workflow_job_template.labels.add(label) ee = ExecutionEnvironment.objects.create(name="barfoo", organization=organization) workflow_job_template.execution_environment = ee ig = InstanceGroup.objects.create(name="bazbar", organization=organization) workflow_job_template.instance_groups.add(ig) workflow_job_template.save() jts = [JobTemplate.objects.create(name='test-jt-{}'.format(i)) for i in range(0, 5)] nodes = [WorkflowJobTemplateNode.objects.create(workflow_job_template=workflow_job_template, unified_job_template=jts[i]) for i in range(0, 5)] nodes[0].success_nodes.add(nodes[1]) nodes[1].success_nodes.add(nodes[2]) nodes[0].failure_nodes.add(nodes[3]) nodes[3].failure_nodes.add(nodes[4]) with mock.patch('awx.api.generics.trigger_delayed_deep_copy') as deep_copy_mock: wfjt_copy_id = post( reverse('api:workflow_job_template_copy', kwargs={'pk': workflow_job_template.pk}), {'name': 'new wfjt name'}, admin, expect=201 ).data['id'] wfjt_copy = type(workflow_job_template).objects.get(pk=wfjt_copy_id) args, kwargs = deep_copy_mock.call_args deep_copy_model_obj(*args, **kwargs) assert wfjt_copy.organization == organization assert wfjt_copy.created_by == admin assert wfjt_copy.name == 'new wfjt name' assert wfjt_copy.labels.count() != 0 assert wfjt_copy.labels.get(pk=label.pk) == label assert wfjt_copy.execution_environment == ee assert wfjt_copy.instance_groups.count() != 0 assert wfjt_copy.instance_groups.get(pk=ig.pk) == ig copied_node_list = [x for x in wfjt_copy.workflow_job_template_nodes.all()] copied_node_list.sort(key=lambda x: int(x.unified_job_template.name[-1])) for node, success_count, failure_count, always_count in zip(copied_node_list, [1, 1, 0, 0, 0], [1, 0, 0, 1, 0], [0, 0, 0, 0, 0]): assert node.success_nodes.count() == success_count assert node.failure_nodes.count() == failure_count assert node.always_nodes.count() == always_count assert copied_node_list[1] in copied_node_list[0].success_nodes.all() assert copied_node_list[2] in copied_node_list[1].success_nodes.all() assert copied_node_list[3] in copied_node_list[0].failure_nodes.all() assert copied_node_list[4] in copied_node_list[3].failure_nodes.all() @pytest.mark.django_db

awx/main/tests/functional/test_copy.py

@pytest.mark.django_db

awx

Python

7de5f772626a00d31026270865276365287cbe37

test_copy.py

test_workflow_job_template_copy

https://github.com/ansible/awx.git

adding test coverage to ensure that FIELDS_TO_PRESERVE_AT_COPY is behaving as expected for WFJTs

def line_collection_2d_to_3d(col, zs=0, zdir='z'): segments3d = _paths_to_3d_segments(col.get_paths(), zs, zdir) col.__class__ = Line3DCollection col.set_segments(segments3d)

lib/mpl_toolkits/mplot3d/art3d.py

matplotlib

Python

df6f95703b60348e01603f98a439b133da2938a0

art3d.py

line_collection_2d_to_3d

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

Improve mpl_toolkit documentation

def rsolve_hyper(coeffs, f, n, **hints): r coeffs = list(map(sympify, coeffs)) f = sympify(f) r, kernel, symbols = len(coeffs) - 1, [], set() if not f.is_zero: if f.is_Add: similar = {} for g in f.expand().args: if not g.is_hypergeometric(n): return None for h in similar.keys(): if hypersimilar(g, h, n): similar[h] += g break else: similar[g] = S.Zero inhomogeneous = [] for g, h in similar.items(): inhomogeneous.append(g + h) elif f.is_hypergeometric(n): inhomogeneous = [f] else: return None for i, g in enumerate(inhomogeneous): coeff, polys = S.One, coeffs[:] denoms = [S.One]*(r + 1) s = hypersimp(g, n) for j in range(1, r + 1): coeff *= s.subs(n, n + j - 1) p, q = coeff.as_numer_denom() polys[j] *= p denoms[j] = q for j in range(r + 1): polys[j] *= Mul(*(denoms[:j] + denoms[j + 1:])) R = rsolve_poly(polys, Mul(*denoms), n) if not (R is None or R is S.Zero): inhomogeneous[i] *= R else: return None result = Add(*inhomogeneous) else: result = S.Zero Z = Dummy('Z') p, q = coeffs[0], coeffs[r].subs(n, n - r + 1) p_factors = [z for z in roots(p, n).keys()] q_factors = [z for z in roots(q, n).keys()] factors = [(S.One, S.One)] for p in p_factors: for q in q_factors: if p.is_integer and q.is_integer and p <= q: continue else: factors += [(n - p, n - q)] p = [(n - p, S.One) for p in p_factors] q = [(S.One, n - q) for q in q_factors] factors = p + factors + q for A, B in factors: polys, degrees = [], [] D = A*B.subs(n, n + r - 1) for i in range(r + 1): a = Mul(*[A.subs(n, n + j) for j in range(i)]) b = Mul(*[B.subs(n, n + j) for j in range(i, r)]) poly = quo(coeffs[i]*a*b, D, n) polys.append(poly.as_poly(n)) if not poly.is_zero: degrees.append(polys[i].degree()) if degrees: d, poly = max(degrees), S.Zero else: return None for i in range(r + 1): coeff = polys[i].nth(d) if coeff is not S.Zero: poly += coeff * Z**i for z in roots(poly, Z).keys(): if z.is_zero: continue recurr_coeffs = [polys[i].as_expr()*z**i for i in range(r + 1)] if d == 0 and 0 != Add(*[recurr_coeffs[j]*j for j in range(1, r + 1)]): # faster inline check (than calling rsolve_poly) for a # constant solution to a constant coefficient recurrence. sol = [Symbol("C" + str(len(symbols)))] else: sol, syms = rsolve_poly(recurr_coeffs, 0, n, len(symbols), symbols=True) sol = sol.collect(syms) sol = [sol.coeff(s) for s in syms] for C in sol: ratio = z * A * C.subs(n, n + 1) / B / C ratio = simplify(ratio) # If there is a nonnegative root in the denominator of the ratio, # this indicates that the term y(n_root) is zero, and one should # start the product with the term y(n_root + 1). n0 = 0 for n_root in roots(ratio.as_numer_denom()[1], n).keys(): if n_root.has(I): return None elif (n0 < (n_root + 1)) == True: n0 = n_root + 1 K = product(ratio, (n, n0, n - 1)) if K.has(factorial, FallingFactorial, RisingFactorial): K = simplify(K) if casoratian(kernel + [K], n, zero=False) != 0: kernel.append(K) kernel.sort(key=default_sort_key) sk = list(zip(numbered_symbols('C'), kernel)) if sk: for C, ker in sk: result += C * ker else: return None if hints.get('symbols', False): # XXX: This returns the symbols in a non-deterministic order symbols |= {s for s, k in sk} return (result, list(symbols)) else: return result

sympy/solvers/recurr.py

sympy

Python

4a7c0c31501685f9d8e6572fe735b592a1fa3c33

recurr.py

rsolve_hyper

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

rsolve_hyper: take into account degenerate solutions This fixes sympy/sympy#8697: In [2]: rsolve(a(n + 3) - a(n + 2) - a(n + 1) + a(n), a(n)) Out[2]: n (-1) ⋅C₁ + C₀ + C₂⋅n Added also test from issue thread, which is not related to the problem. And from PR request diofant/diofant#442. Test for sympy/sympy#6844 was adapted.

def parse_targets(self, source): self.dist_log("looking for '@targets' inside -> ", source) # get lines between /*@targets and */ with open(source) as fd: tokens = "" max_to_reach = 1000 # good enough, isn't? start_with = "@targets" start_pos = -1 end_with = "*/" end_pos = -1 for current_line, line in enumerate(fd): if current_line == max_to_reach: self.dist_fatal("reached the max of lines") break if start_pos == -1: start_pos = line.find(start_with) if start_pos == -1: continue start_pos += len(start_with) tokens += line end_pos = line.find(end_with) if end_pos != -1: end_pos += len(tokens) - len(line) break if start_pos == -1: self.dist_fatal("expected to find '%s' within a C comment" % start_with) if end_pos == -1: self.dist_fatal("expected to end with '%s'" % end_with) tokens = tokens[start_pos:end_pos] return self._parse_target_tokens(tokens) _parse_regex_arg = re.compile(r'\s|,|([+-])')

numpy/distutils/ccompiler_opt.py

numpy

Python

f404e9e92e87a3990712d723d5c562a89300ac01

ccompiler_opt.py

parse_targets

https://github.com/numpy/numpy.git

Add space after argument name

def data_system_ping_fixture(): return json.loads(load_fixture("system_ping_data.json", "guardian")) @pytest.fixture(name="data_valve_status", scope="session")

tests/components/guardian/conftest.py

@pytest.fixture(name="data_valve_status", scope="session")

core

Python

6bbe38578c74e5ecd8aadcd2cf39cddca8a59a52

conftest.py

data_system_ping_fixture

https://github.com/home-assistant/core.git

Add diagnostics to Elexa Guardian (#64599)

def _softmax(x, axis): if not dtypes.issubdtype(x.dtype, np.floating): raise TypeError(f"_softmax only accepts floating dtypes, got {x.dtype}") x_max = jnp.max(x, axis, keepdims=True) unnormalized = jnp.exp(x - lax.stop_gradient(x_max)) return unnormalized / unnormalized.sum(axis, keepdims=True)

jax/_src/random.py

jax

Python

69969ef8031e424b19dd020a396b3fbdc25b703e

random.py

_softmax

https://github.com/google/jax.git

add random.loggamma and improve dirichlet & beta implementation

def fit(self, X, y=None): if not self.degree >= 1: raise ValueError(f"degree={self.degree} should be >=1.") X = self._validate_data(X, accept_sparse="csc") random_state = check_random_state(self.random_state) n_features = X.shape[1] if self.coef0 != 0: n_features += 1 self.indexHash_ = random_state.randint( 0, high=self.n_components, size=(self.degree, n_features) ) self.bitHash_ = random_state.choice(a=[-1, 1], size=(self.degree, n_features)) self._n_features_out = self.n_components return self

sklearn/kernel_approximation.py

scikit-learn

Python

d616e43947340e152e4a901931e954d699368fa9

kernel_approximation.py

fit

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

ENH Adds feature_names_out for most of kernel_approximation (#22694)

def _poll_with_exponential_delay(request, execute_num_retries, max_n, is_done_func, is_error_func): for i in range(0, max_n): try: response = request.execute(num_retries=execute_num_retries) if is_error_func(response): raise ValueError(f'The response contained an error: {response}') if is_done_func(response): log.info('Operation is done: %s', response) return response time.sleep((2 ** i) + (random.randint(0, 1000) / 1000)) except HttpError as e: if e.resp.status != 429: log.info('Something went wrong. Not retrying: %s', format(e)) raise else: time.sleep((2 ** i) + (random.randint(0, 1000) / 1000)) raise ValueError(f'Connection could not be established after {max_n} retries.')

airflow/providers/google/cloud/hooks/mlengine.py

airflow

Python

602abe8394fafe7de54df7e73af56de848cdf617

mlengine.py

_poll_with_exponential_delay

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

Remove `:type` lines now sphinx-autoapi supports typehints (#20951) * Remove `:type` lines now sphinx-autoapi supports typehints Since we have no updated sphinx-autoapi to a more recent version it supports showing type hints in the documentation, so we don't need to have the type hints _and_ the `:type` lines -- which is good, as the ones in the doc strings are easy to get out of date! The following settings have been set: `autodoc_typehints = 'description'` -- show types in description (where previous `:type` used to show up) `autodoc_typehints_description_target = 'documented'` -- only link to types that are documented. (Without this we have some missing return types that aren't documented, and aren't linked to in our current python API docs, so this caused a build failure) `autodoc_typehints_format = 'short'` -- Shorten type hints where possible, i.e. `StringIO` instead of `io.StringIO` * Add argument type names to local spelling dictionary Now that we are using the type hints in the docs, sphinxcontrib-spelling picks them up as words to be checked, so we have to ignore them. I've chosen to add the provider specific ones to local dictionary files rather than the global, as for example, `mgmt` is an error in most places, but not in some of the Azure provider.

def get_change_message(self): if self.change_message and self.change_message[0] == "[": try: change_message = json.loads(self.change_message) except json.JSONDecodeError: return self.change_message messages = [] for sub_message in change_message: if "added" in sub_message: if sub_message["added"]: sub_message["added"]["name"] = gettext( sub_message["added"]["name"] ) messages.append( gettext("Added {name} “{object}”.").format( **sub_message["added"] ) ) else: messages.append(gettext("Added.")) elif "changed" in sub_message: sub_message["changed"]["fields"] = get_text_list( [ gettext(field_name) for field_name in sub_message["changed"]["fields"] ], gettext("and"), ) if "name" in sub_message["changed"]: sub_message["changed"]["name"] = gettext( sub_message["changed"]["name"] ) messages.append( gettext("Changed {fields} for {name} “{object}”.").format( **sub_message["changed"] ) ) else: messages.append( gettext("Changed {fields}.").format( **sub_message["changed"] ) ) elif "deleted" in sub_message: sub_message["deleted"]["name"] = gettext( sub_message["deleted"]["name"] ) messages.append( gettext("Deleted {name} “{object}”.").format( **sub_message["deleted"] ) ) change_message = " ".join(msg[0].upper() + msg[1:] for msg in messages) return change_message or gettext("No fields changed.") else: return self.change_message

django/contrib/admin/models.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

models.py

get_change_message

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

Refs #33476 -- Reformatted code with Black.

def call_ef(self, other_args): parser = argparse.ArgumentParser( add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, prog="ef", description=, ) parser.add_argument( "-p", "--period", default=self.params["historic_period"] if "historic_period" in self.params else "3y", dest="historic_period", help=, ) parser.add_argument( "-s", "--start", default=self.params["start_period"] if "start_period" in self.params else "", dest="start_period", help=, ) parser.add_argument( "-e", "--end", default=self.params["end_period"] if "end_period" in self.params else "", dest="end_period", help=, ) parser.add_argument( "-lr", "--log-returns", action="store_true", default=self.params["log_returns"] if "log_returns" in self.params else False, dest="log_returns", help="If use logarithmic or arithmetic returns to calculate returns", ) parser.add_argument( "-f", "--freq", default=self.params["return_frequency"] if "return_frequency" in self.params else "d", dest="return_frequency", help=, choices=self.FREQ_CHOICES, ) parser.add_argument( "-mn", "--maxnan", type=float, default=self.params["max_nan"] if "max_nan" in self.params else 0.05, dest="max_nan", help=, ) parser.add_argument( "-th", "--threshold", type=float, default=self.params["threshold_value"] if "threshold_value" in self.params else 0.30, dest="threshold_value", help=, ) parser.add_argument( "-mt", "--method", default=self.params["nan_fill_method"] if "nan_fill_method" in self.params else "time", dest="nan_fill_method", help=, ) parser.add_argument( "-rm", "--risk-measure", default=self.params["risk_measure"] if "risk_measure" in self.params else "MV", dest="risk_measure", help=, choices=self.MEAN_RISK_CHOICES, ) parser.add_argument( "-r", "--risk-free-rate", type=float, dest="risk_free", default=self.params["risk_free"] if "risk_free" in self.params else get_rf(), help=, ) parser.add_argument( "-a", "--alpha", type=float, default=self.params["significance_level"] if "significance_level" in self.params else 0.05, dest="significance_level", help="Significance level of CVaR, EVaR, CDaR and EDaR", ) parser.add_argument( "-v", "--value", dest="long_allocation", help="Amount to allocate to portfolio in long positions", type=float, default=self.params["long_allocation"] if "long_allocation" in self.params else 1, ) parser.add_argument( "-vs", "--value-short", dest="short_allocation", help="Amount to allocate to portfolio in short positions", type=float, default=self.params["short_allocation"] if "short_allocation" in self.params else 0.0, ) if other_args and "-" not in other_args[0][0]: other_args.insert(0, "-n") parser.add_argument( "-n", "--number-portfolios", default=self.params["amount_portfolios"] if "amount_portfolios" in self.params else 100, type=check_non_negative, dest="amount_portfolios", help="Number of portfolios to simulate", ) parser.add_argument( "-se", "--seed", default=self.params["random_seed"] if "random_seed" in self.params else 123, type=check_non_negative, dest="random_seed", help="Seed used to generate random portfolios", ) parser.add_argument( "-t", "--tangency", action="store_true", dest="tangency", default=self.params["tangency"] if "tangency" in self.params else False, help="Adds the optimal line with the risk-free asset", ) ns_parser = parse_known_args_and_warn(parser, other_args) if ns_parser: if len(self.tickers) < 2: console.print( "Please have at least 2 loaded tickers to calculate weights.\n" ) return optimizer_view.display_ef( stocks=self.tickers, period=ns_parser.historic_period, start=ns_parser.start_period, end=ns_parser.end_period, log_returns=ns_parser.log_returns, freq=ns_parser.return_frequency, maxnan=ns_parser.max_nan, threshold=ns_parser.threshold_value, method=ns_parser.nan_fill_method, risk_measure=ns_parser.risk_measure.lower(), risk_free_rate=ns_parser.risk_free, alpha=ns_parser.significance_level, value=ns_parser.long_allocation, value_short=ns_parser.short_allocation, n_portfolios=ns_parser.amount_portfolios, seed=ns_parser.random_seed, tangency=ns_parser.tangency, )

openbb_terminal/portfolio/portfolio_optimization/po_controller.py

OpenBBTerminal

Python

34bc290dded1bd2418fc3c6b375a79f9cdd68d5a

po_controller.py

call_ef

https://github.com/OpenBB-finance/OpenBBTerminal.git

New portfolio optimization menu (#1642) * New-Portfolio-Optimization-Menu * New-Portfolio-Optimization-Menu * New-Portfolio-Optimization-Menu * Update _index.md * New-Portfolio-Optimization-Menu * New-Portfolio-Optimization-Menu * configure portfolio optimization parameters ini * minor improvement * Revert "New-Portfolio-Optimization-Menu" This reverts commit b4b7169cfbc8f28c379eb1920307c2cdd2e47a0f. * Add in Excel functionality and improve the capabilities * Add Excel load function * Tidying up the functions * New-Portfolio-Optimization-Menu * New-Portfolio-Optimization-Menu * New-Portfolio-Optimization-Menu * Re-add my code * Some spacing and details * Add folder structure for portfolio * Update terminal file loading * New-Portfolio-Optimization-Menu * Make it possible to move from params to po with loaded file * New-Portfolio-Optimization-Menu * New-Portfolio-Optimization-Menu * Making the connection between the parameters file and the functions * Add in allocation and new params files * Improve params default settings * New-Portfolio-Optimization-Menu * Update Portfolios and Params sheets * Update sheets * Update command to load in correct sheet * Adjust function to only read specific columns * Update portfolio * Small correction * New-Portfolio-Optimization-Menu * New-Portfolio-Optimization-Menu * New-Portfolio-Optimization-Menu * Patched up show error * Add Equity portfolio * Make functions more robust * New-Portfolio-Optimization-Menu * New-Portfolio-Optimization-Menu * Add in Params documentation * Fixing Linting * Add in Requirements and Poetry Updates * Update website * linting * Update tests * Minor fix * remove unneccesary READMEs * Remove expected variable type * Improve documentation * Clean up the code * Refractoring * Adjust names to make it OS friendly Co-authored-by: Jeroen Bouma <[email protected]> Co-authored-by: jmaslek <[email protected]> Co-authored-by: Colin Delahunty <[email protected]> Co-authored-by: DidierRLopes <[email protected]>

def get(cls): min_partition_size = super().get() assert min_partition_size > 0, "`min_partition_size` should be > 0" return min_partition_size

modin/config/envvars.py

modin

Python

0bdc482d6f1682e103b4c4d7ee7c4d505d2d3b1c

envvars.py

get

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

REFACTOR-#3768: change 'compute_chunksize' signature (#3769) Co-authored-by: Yaroslav Igoshev <[email protected]> Signed-off-by: Anatoly Myachev <[email protected]>

def _get_loss(self): return HalfSquaredError() # TODO(1.3): remove

sklearn/linear_model/_glm/glm.py

scikit-learn

Python

75a94f518f7bd7d0bf581ffb67d9f961e3c4efbc

glm.py

_get_loss

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

ENH migrate GLMs / TweedieRegressor to linear loss (#22548) Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: Thomas J. Fan <[email protected]>

def update_last_purchase_rate(doc, is_submit): import frappe.utils this_purchase_date = frappe.utils.getdate(doc.get("posting_date") or doc.get("transaction_date")) for d in doc.get("items"): # get last purchase details last_purchase_details = get_last_purchase_details(d.item_code, doc.name) # compare last purchase date and this transaction's date last_purchase_rate = None if last_purchase_details and ( doc.get("docstatus") == 2 or last_purchase_details.purchase_date > this_purchase_date ): last_purchase_rate = last_purchase_details["base_net_rate"] elif is_submit == 1: # even if this transaction is the latest one, it should be submitted # for it to be considered for latest purchase rate if flt(d.conversion_factor): last_purchase_rate = flt(d.base_net_rate) / flt(d.conversion_factor) # Check if item code is present # Conversion factor should not be mandatory for non itemized items elif d.item_code: frappe.throw(_("UOM Conversion factor is required in row {0}").format(d.idx)) # update last purchsae rate frappe.db.set_value("Item", d.item_code, "last_purchase_rate", flt(last_purchase_rate))

erpnext/buying/utils.py

erpnext

Python

494bd9ef78313436f0424b918f200dab8fc7c20b

utils.py

update_last_purchase_rate

https://github.com/frappe/erpnext.git

style: format code with black

def clean_subpage_models(cls): if cls._clean_subpage_models is None: subpage_types = getattr(cls, "subpage_types", None) if subpage_types is None: # if subpage_types is not specified on the Page class, allow all page types as subpages cls._clean_subpage_models = get_page_models() else: cls._clean_subpage_models = [ resolve_model_string(model_string, cls._meta.app_label) for model_string in subpage_types ] for model in cls._clean_subpage_models: if not issubclass(model, Page): raise LookupError("%s is not a Page subclass" % model) return cls._clean_subpage_models

wagtail/core/models/__init__.py

wagtail

Python

d10f15e55806c6944827d801cd9c2d53f5da4186

__init__.py

clean_subpage_models

https://github.com/wagtail/wagtail.git

Reformat with black

def test_pandas_contiguous_dtypes(): pd = pytest.importorskip("pandas") df1 = pd.DataFrame([[1, 2.2], [3, 4.4]]) df2 = pd.DataFrame([[1.1, 2.2], [3.3, 4.4]]) assert sizeof(df2) < sizeof(df1)

dask/tests/test_sizeof.py

dask

Python

80dd84d46ef6b7befa1b416c4597c83ef81ef972

test_sizeof.py

test_pandas_contiguous_dtypes

https://github.com/dask/dask.git

Deflate sizeof() of duplicate references to pandas object types (#9776)

def namelengthsrc(self): return self["namelengthsrc"]

packages/python/plotly/plotly/graph_objs/bar/_hoverlabel.py

plotly.py

Python

43e3a4011080911901176aab919c0ecf5046ddd3

_hoverlabel.py

namelengthsrc

https://github.com/plotly/plotly.py.git

switch to black .22

def list(self, directory='""', pattern='*'): name = 'LIST' typ, dat = self._simple_command(name, directory, pattern) return self._untagged_response(typ, dat, name)

python3.10.4/Lib/imaplib.py

XX-Net

Python

8198943edd73a363c266633e1aa5b2a9e9c9f526

imaplib.py

list

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

add python 3.10.4 for windows

def deploy(self) -> Pipeline: [node.deploy() for node in self._incoming_edges] self._executor = create_executor_from_step_config( self._serialized_callable_factory, self._config ) return Pipeline(self)

python/ray/serve/pipeline/node.py

ray

Python

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

node.py

deploy

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

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

async def test_only_migrate_once(hass, utcnow): entity_registry = er.async_get(hass) aid = get_next_aid() old_light_entry = entity_registry.async_get_or_create( "light", "homekit_controller", f"homekit-00:00:00:00:00:00-{aid}-8", ) new_light_entry = entity_registry.async_get_or_create( "light", "homekit_controller", f"00:00:00:00:00:00_{aid}_8", ) await setup_test_component(hass, create_lightbulb_service_with_color_temp) assert ( entity_registry.async_get(old_light_entry.entity_id).unique_id == f"homekit-00:00:00:00:00:00-{aid}-8" ) assert ( entity_registry.async_get(new_light_entry.entity_id).unique_id == f"00:00:00:00:00:00_{aid}_8" )

tests/components/homekit_controller/test_light.py

core

Python

f23b1750e85f07091eb896a0b12b8f95e5646338

test_light.py

test_only_migrate_once

https://github.com/home-assistant/core.git

Migrate HomeKit Controller to use stable identifiers (#80064)

def expectation(self, expr, condition=None, evaluate=True, **kwargs): return _SubstituteRV._expectation(expr, condition, evaluate, **kwargs)

sympy/stats/stochastic_process_types.py

sympy

Python

7fe8e027ae1d7f683243c0229b961671a6cbb4c5

stochastic_process_types.py

expectation

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

Improved some documentation in the stats module

def slice_inputs(self, indices_dataset, inputs): dataset = tf.data.Dataset.zip( (indices_dataset, tf.data.Dataset.from_tensors(inputs).repeat()) )

keras/engine/data_adapter.py

keras

Python

84afc5193d38057e2e2badf9c889ea87d80d8fbf

data_adapter.py

slice_inputs

https://github.com/keras-team/keras.git

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

def operation(self): return self["operation"]

packages/python/plotly/plotly/graph_objs/contour/_contours.py

plotly.py

Python

43e3a4011080911901176aab919c0ecf5046ddd3

_contours.py

operation

https://github.com/plotly/plotly.py.git

switch to black .22

def test_generic_inline_model_admin_bad_fk_field(self):

tests/admin_checks/tests.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

tests.py

test_generic_inline_model_admin_bad_fk_field

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

Refs #33476 -- Reformatted code with Black.

def evaluate(self, expr, context): if isinstance(expr, string_types): if expr[0] in '\'"': result = expr[1:-1] else: if expr not in context: raise SyntaxError('unknown variable: %s' % expr) result = context[expr] else: assert isinstance(expr, dict) op = expr['op'] if op not in self.operations: raise NotImplementedError('op not implemented: %s' % op) elhs = expr['lhs'] erhs = expr['rhs'] if _is_literal(expr['lhs']) and _is_literal(expr['rhs']): raise SyntaxError('invalid comparison: %s %s %s' % (elhs, op, erhs)) lhs = self.evaluate(elhs, context) rhs = self.evaluate(erhs, context) if ((elhs == 'python_version' or erhs == 'python_version') and op in ('<', '<=', '>', '>=', '===', '==', '!=', '~=')): lhs = NV(lhs) rhs = NV(rhs) elif elhs == 'python_version' and op in ('in', 'not in'): lhs = NV(lhs) rhs = _get_versions(rhs) result = self.operations[op](lhs, rhs) return result

pipenv/patched/notpip/_vendor/distlib/markers.py

pipenv

Python

f3166e673fe8d40277b804d35d77dcdb760fc3b3

markers.py

evaluate

https://github.com/pypa/pipenv.git

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

def _is_function_class_equation(func_class, f, symbol): if f.is_Mul or f.is_Add: return all(_is_function_class_equation(func_class, arg, symbol) for arg in f.args) if f.is_Pow: if not f.exp.has(symbol): return _is_function_class_equation(func_class, f.base, symbol) else: return False if not f.has(symbol): return True if isinstance(f, func_class): try: g = Poly(f.args[0], symbol) return g.degree() <= 1 except PolynomialError: return False else: return False

sympy/solvers/solveset.py

sympy

Python

e0dc14eca132f37c5f49369eb4051eae37c9b119

solveset.py

_is_function_class_equation

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

Refactored import ordering in functions

def update_connected_interfaces(instance, created, raw=False, **kwargs): logger = logging.getLogger('netbox.wireless.wirelesslink') if raw: logger.debug(f"Skipping endpoint updates for imported wireless link {instance}") return if instance.interface_a.wireless_link != instance: logger.debug(f"Updating interface A for wireless link {instance}") instance.interface_a.wireless_link = instance instance.interface_a._link_peer = instance.interface_b instance.interface_a.save() if instance.interface_b.cable != instance: logger.debug(f"Updating interface B for wireless link {instance}") instance.interface_b.wireless_link = instance instance.interface_b._link_peer = instance.interface_a instance.interface_b.save() # Create/update cable paths if created: for interface in (instance.interface_a, instance.interface_b): create_cablepath([interface]) @receiver(post_delete, sender=WirelessLink)

netbox/wireless/signals.py

@receiver(post_delete, sender=WirelessLink)

netbox

Python

951627093c11584ffb73ad2be2aef40a91a90934

signals.py

update_connected_interfaces

https://github.com/netbox-community/netbox.git

Test cleanup

def get_payload(self, i=None, decode=False): # Here is the logic table for this code, based on the email5.0.0 code: # i decode is_multipart result # ------ ------ ------------ ------------------------------ # None True True None # i True True None # None False True _payload (a list) # i False True _payload element i (a Message) # i False False error (not a list) # i True False error (not a list) # None False False _payload # None True False _payload decoded (bytes) # Note that Barry planned to factor out the 'decode' case, but that # isn't so easy now that we handle the 8 bit data, which needs to be # converted in both the decode and non-decode path. if self.is_multipart(): if decode: return None if i is None: return self._payload else: return self._payload[i] # For backward compatibility, Use isinstance and this error message # instead of the more logical is_multipart test. if i is not None and not isinstance(self._payload, list): raise TypeError('Expected list, got %s' % type(self._payload)) payload = self._payload # cte might be a Header, so for now stringify it. cte = str(self.get('content-transfer-encoding', '')).lower() # payload may be bytes here. if isinstance(payload, str): if utils._has_surrogates(payload): bpayload = payload.encode('ascii', 'surrogateescape') if not decode: try: payload = bpayload.decode(self.get_param('charset', 'ascii'), 'replace') except LookupError: payload = bpayload.decode('ascii', 'replace') elif decode: try: bpayload = payload.encode('ascii') except UnicodeError: # This won't happen for RFC compliant messages (messages # containing only ASCII code points in the unicode input). # If it does happen, turn the string into bytes in a way # guaranteed not to fail. bpayload = payload.encode('raw-unicode-escape') if not decode: return payload if cte == 'quoted-printable': return quopri.decodestring(bpayload) elif cte == 'base64': # XXX: this is a bit of a hack; decode_b should probably be factored # out somewhere, but I haven't figured out where yet. value, defects = decode_b(b''.join(bpayload.splitlines())) for defect in defects: self.policy.handle_defect(self, defect) return value elif cte in ('x-uuencode', 'uuencode', 'uue', 'x-uue'): in_file = BytesIO(bpayload) out_file = BytesIO() try: uu.decode(in_file, out_file, quiet=True) return out_file.getvalue() except uu.Error: # Some decoding problem return bpayload if isinstance(payload, str): return bpayload return payload

python3.10.4/Lib/email/message.py

XX-Net

Python

8198943edd73a363c266633e1aa5b2a9e9c9f526

message.py

get_payload

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

add python 3.10.4 for windows

def notched(self): return self["notched"]

packages/python/plotly/plotly/graph_objs/_box.py

plotly.py

Python

43e3a4011080911901176aab919c0ecf5046ddd3

_box.py

notched

https://github.com/plotly/plotly.py.git

switch to black .22

def solve_biquadratic(f, g, opt): G = groebner([f, g]) if len(G) == 1 and G[0].is_ground: return None if len(G) != 2: raise SolveFailed x, y = opt.gens p, q = G if not p.gcd(q).is_ground: # not 0-dimensional raise SolveFailed p = Poly(p, x, expand=False) p_roots = [rcollect(expr, y) for expr in roots(p).keys()] q = q.ltrim(-1) q_roots = list(roots(q).keys()) solutions = [] for q_root in q_roots: for p_root in p_roots: solution = (p_root.subs(y, q_root), q_root) solutions.append(solution) return sorted(solutions, key=default_sort_key)

sympy/solvers/polysys.py

sympy

Python

59d22b6bb7287613d598611027f640d068ca5748

polysys.py

solve_biquadratic

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

Moved imports to higher level

def _get_metric_object(self, metric, y_t, y_p): if metric is None: return None # Ok to have no metric for an output. # Convenience feature for selecting b/t binary, categorical, # and sparse categorical. if str(metric).lower() not in ["accuracy", "acc", "crossentropy", "ce"]: metric_obj = metrics_mod.get(metric) else: y_t_rank = len(y_t.shape.as_list()) y_p_rank = len(y_p.shape.as_list()) y_t_last_dim = y_t.shape.as_list()[-1] y_p_last_dim = y_p.shape.as_list()[-1] is_binary = y_p_last_dim == 1 is_sparse_categorical = ( y_t_rank < y_p_rank or y_t_last_dim == 1 and y_p_last_dim > 1 ) if str(metric).lower() in ["accuracy", "acc"]: if is_binary: metric_obj = metrics_mod.binary_accuracy elif is_sparse_categorical: metric_obj = metrics_mod.sparse_categorical_accuracy else: metric_obj = metrics_mod.categorical_accuracy else: if is_binary: metric_obj = metrics_mod.binary_crossentropy elif is_sparse_categorical: metric_obj = metrics_mod.sparse_categorical_crossentropy else: metric_obj = metrics_mod.categorical_crossentropy if isinstance(metric_obj, losses_mod.Loss): metric_obj._allow_sum_over_batch_size = ( True # pylint: disable=protected-access ) if not isinstance(metric_obj, metrics_mod.Metric): if isinstance(metric, str): metric_name = metric else: metric_name = get_custom_object_name(metric) if metric_name is None: raise ValueError( f"Metric should be a callable, received: {metric}" ) metric_obj = metrics_mod.MeanMetricWrapper( metric_obj, name=metric_name ) return metric_obj

keras/engine/compile_utils.py

keras

Python

84afc5193d38057e2e2badf9c889ea87d80d8fbf

compile_utils.py

_get_metric_object

https://github.com/keras-team/keras.git

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

def test_visibility_when_disabled(self) -> None: room_id = self.helper.create_room_as(self.user_id, tok=self.token) self.helper.send_state( room_id=room_id, event_type=EventTypes.Retention, body={"max_lifetime": one_day_ms}, tok=self.token, ) resp = self.helper.send(room_id=room_id, body="test", tok=self.token) self.reactor.advance(one_day_ms * 2 / 1000) self.get_event(room_id, resp["event_id"])

tests/rest/client/test_retention.py

synapse

Python

4cc4229cd7a55d2556c798fecbb1c9660dc821c8

test_retention.py

test_visibility_when_disabled

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

Prevent expired events from being filtered out when retention is disabled (#12611) Co-authored-by: Richard van der Hoff <[email protected]> Co-authored-by: Patrick Cloke <[email protected]>

def __eq__(self, other): if self is other: return True if not isinstance(other, Basic): return self._do_eq_sympify(other) # check for pure number expr if not (self.is_Number and other.is_Number) and ( type(self) != type(other)): return False a, b = self._hashable_content(), other._hashable_content() if a != b: return False # check number *in* an expression for a, b in zip(a, b): if not isinstance(a, Basic): continue if a.is_Number and type(a) != type(b): return False return True

sympy/core/basic.py

sympy

Python

f5ef4e62e5cb5637f2bf2af0ee73e43c58c33c25

basic.py

__eq__

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

core/basic: Basic.__eq__ only performs user defined conversions core/evalf: no longer create unneeded Tuples with None arguments Fixes #22581 only use _sympify in __eq__ when needed defined _converter and updated Boolean comparisons removed try-except for sympify because it should always be possible at that point completely split sympy and external converters checking entire mro use the relevant part of sympify directly type from copy paste removed ambiguous try-except blocks changed resolve order for sympy/user converters and mro updated documentation typo

def check_for_updates(): version_message = get_update_status() print(version_message)

spotdl/utils/console.py

spotify-downloader

Python

deca40c2e26afed62e1f9ec4be14aff9e125929b

console.py

check_for_updates

https://github.com/spotDL/spotify-downloader.git

moved console actions to a new file

def is_packaged_application() -> bool: return cfg.LOGGING_APP_NAME == "gst_packaged"

openbb_terminal/terminal_helper.py

OpenBBTerminal

Python

eb244a1d8d01e1ad93c5dc349656aa4170397f90

terminal_helper.py

is_packaged_application

https://github.com/OpenBB-finance/OpenBBTerminal.git

Docker : building + publishing (#2904) * fixed integrated test test_stocks_ba.openbb * fixed integrated test test_stocks_dd.openbb * improved and centralised the check * fix lint * Docker : update ci + build files * Docker : update build and CD * Docker : update CD * Docker : test * Docker : test CD * Docker : test CD * Docker : rename `build.sh` * Docker : tests CD * Docker : test CD * Docker : update CD + build * Docker : fix CD * Docker : fix CD * Docker : build * Docker : test CD * Docker : CD * Docker : CD * Docker : test * Docker : test CD * Docker : test CD * Docker : test CD * Docker : test CD * Docker : test CD * Docker : test CD * Docker : test CD * Docker : test CD * Docker : build + CD * Docker : test CD * Docker : test CD * Docker : test CD * Docker : test CD * Docker : build * Docker : test CD * Docker : build + cd * Moving `scripts` * Checkout `helper_funcs.py` from main * Docker : remove test file with alpine * fixing readme errors * fixing missed readme errors * Docker : build * Logging : handle docker app name * Docker : test CD * Docker : cd * Doc * Doc * Doc : linting * Doc * Docker * Doc * Fixing `terminal_controller` * Linting * Doc : fixing links * Version 1.9.1 * Docker : fix name * Doc : add volumes in command Co-authored-by: hjoaquim <[email protected]> Co-authored-by: James Simmons <[email protected]> Co-authored-by: Colin Delahunty <[email protected]>

def _optimize_stages(self): context = DatasetContext.get_current() if not context.optimize_fuse_stages: self._optimized_stages = self._stages return # This dummy dataset will be used to get a set of optimized stages. dummy_ds = Dataset( ExecutionPlan(BlockList([], []), DatasetStats(stages={}, parent=None)), 0, True, used_from_dataset_pipeline=True, ) # Apply all pipeline operations to the dummy dataset. for stage in self._stages: dummy_ds = stage(dummy_ds) # Get the optimized stages. _, _, stages = dummy_ds._plan._optimize() # Apply these optimized stages to the datasets underlying the pipeline. # These optimized stages will be executed by the PipelineExecutor. optimized_stages = [] for stage in stages: optimized_stages.append( lambda ds, stage=stage: Dataset( ds._plan.with_stage(stage), ds._epoch, True, used_from_dataset_pipeline=True, ) ) self._optimized_stages = optimized_stages

python/ray/data/dataset_pipeline.py

ray

Python

45ba0e3cacbf4f38b9724437798c75341c2ddc7c

dataset_pipeline.py

_optimize_stages

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

Object GC for block splitting inside the dataset splitting (#26196) The pipeline will spill objects when splitting the dataset into multiple equal parts. Co-authored-by: Ubuntu <[email protected]>

def vertices_loss(criterion_vertices, pred_vertices, gt_vertices, has_smpl): pred_vertices_with_shape = pred_vertices[has_smpl == 1] gt_vertices_with_shape = gt_vertices[has_smpl == 1] if len(gt_vertices_with_shape) > 0: return criterion_vertices(pred_vertices_with_shape, gt_vertices_with_shape) else: return paddle.to_tensor([1.]).fill_(0.) @register @serializable

ppdet/modeling/losses/pose3d_loss.py

@register @serializable

PaddleDetection

Python

d4e34fe165c09db65fd00113708be1b711ac957c

pose3d_loss.py

vertices_loss

https://github.com/PaddlePaddle/PaddleDetection.git

pose3d metro modeling (#6612) * pose3d metro modeling * delete extra comments

def step(self): if self._implements_method("_train") and log_once("_train"): raise DeprecationWarning( "Trainable._train is deprecated and is now removed. Override " "Trainable.step instead." ) raise NotImplementedError

python/ray/tune/trainable.py

ray

Python

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

trainable.py

step

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

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

def print_help(self): help_text = console.print(text=help_text, menu="Cryptocurrency - Discovery")

gamestonk_terminal/cryptocurrency/discovery/discovery_controller.py

OpenBBTerminal

Python

82747072c511beb1b2672846ae2ee4aec53eb562

discovery_controller.py

print_help

https://github.com/OpenBB-finance/OpenBBTerminal.git

Terminal Wide Rich (#1161) * My idea for how we handle Rich moving forward * remove independent consoles * FIxed pylint issues * add a few vars * Switched print to console * More transitions * Changed more prints * Replaced all prints * Fixing tabulate * Finished replace tabulate * Finished removing rich from Tabulate * add Panel around menu * add GST watermark under feature flag * Fixed 46 tests * Delete test_screener[False].yaml * Delete test_screener[True].yaml * Fixed the rest of the tests * add help and source color vars and use rgb * rich on stocks/options * update rich on disc, dps, sia * rich in gov, ins and scr menus * ba and ca menus with rich * Fixed import issue * Fixed some tests * removed termcolor * Removed prettytable * add rich to remaining stocks menus * FIxed linting issue * Added James' changes * Updated dependencies * Add rich to cryptocurrency menu * refactor economy and forex * refactor etf with rich * refactor mfunds * refactor rich rest * not specify style so default color works well on any background * Fixing mypy issues * Updated tests * More test fixes * James' test fixes * Updating tests : stocks/screener - fix cassettes using BR * Updating tests : crypto * Updating tests : disable DEBUG_MODE * Updating tests : stocks/fa/yfinance * minor fixes that escape * Improve the rich table function (that replaces tabulate :D ) * Fixed bad code * delete rogue file + dcf fix + NoConsole * sia mypy * fuck you linter * fuck you linter pt 2 * skip hehe * i hate the black linter * ubuntu mypy attempt * Update : rich_config + gtff * Updating tests : conftest * Updating tests : stocks * Update : rich_config * Updating : rich_config * make panel configurable for Theodore :b * colors update * Merged * Updating : rich_config + feature_flags * Updating : rich_config * Updating tests : stocks * Updating : feature_flags Co-authored-by: DidierRLopes <[email protected]> Co-authored-by: Chavithra PARANA <[email protected]> Co-authored-by: james <[email protected]> Co-authored-by: jose-donato <[email protected]>

def _rpc_stats(self) -> Dict[str, Any]:

freqtrade/rpc/rpc.py

freqtrade

Python

be84a028c18bdbfd58dea8a51b6d59b77b672a8c

rpc.py

_rpc_stats

https://github.com/freqtrade/freqtrade.git

Avoid mixed types in the api for /stats

def preprocess_samples(self): r # sort items based on the sequence length in ascending order text_ignore_idx, text_keep_idx = self.sort_and_filter_by_length(self.text_lengths, self.min_text_len, self.max_text_len) audio_ignore_idx, audio_keep_idx = self.sort_and_filter_by_length(self.audio_lengths, self.min_audio_len, self.max_audio_len) keep_idx = list(set(audio_keep_idx) | set(text_keep_idx)) ignore_idx = list(set(audio_ignore_idx) | set(text_ignore_idx)) samples = [] for idx in keep_idx: samples.append(self.samples[idx]) if len(samples) == 0: raise RuntimeError(" [!] No samples left") # shuffle batch groups # create batches with similar length items # the larger the `batch_group_size`, the higher the length variety in a batch. samples = self.create_buckets(samples, self.batch_group_size) # update items to the new sorted items self.samples = samples if self.verbose: print(" | > Preprocessing samples") print(" | > Max text length: {}".format(np.max(self.text_lengths))) print(" | > Min text length: {}".format(np.min(self.text_lengths))) print(" | > Avg text length: {}".format(np.mean(self.text_lengths))) print(" | ") print(" | > Max audio length: {}".format(np.max(self.audio_lengths))) print(" | > Min audio length: {}".format(np.min(self.audio_lengths))) print(" | > Avg audio length: {}".format(np.mean(self.audio_lengths))) print(f" | > Num. instances discarded samples: {len(ignore_idx)}") print(" | > Batch group size: {}.".format(self.batch_group_size))

TTS/tts/datasets/dataset.py

TTS

Python

176b712c1a40cf630da9a77f1826836723c40fde

dataset.py

preprocess_samples

https://github.com/coqui-ai/TTS.git

Refactor TTSDataset ⚡️

def selectedpoints(self): return self["selectedpoints"]

packages/python/plotly/plotly/graph_objs/_bar.py

plotly.py

Python

43e3a4011080911901176aab919c0ecf5046ddd3

_bar.py

selectedpoints

https://github.com/plotly/plotly.py.git

switch to black .22

def make_release_tree(self, base_dir, files): # Create all the directories under 'base_dir' necessary to # put 'files' there; the 'mkpath()' is just so we don't die # if the manifest happens to be empty. self.mkpath(base_dir) dir_util.create_tree(base_dir, files, dry_run=self.dry_run) # And walk over the list of files, either making a hard link (if # os.link exists) to each one that doesn't already exist in its # corresponding location under 'base_dir', or copying each file # that's out-of-date in 'base_dir'. (Usually, all files will be # out-of-date, because by default we blow away 'base_dir' when # we're done making the distribution archives.) if hasattr(os, 'link'): # can make hard links on this system link = 'hard' msg = "making hard links in %s..." % base_dir else: # nope, have to copy link = None msg = "copying files to %s..." % base_dir if not files: log.warn("no files to distribute -- empty manifest?") else: log.info(msg) for file in files: if not os.path.isfile(file): log.warn("'%s' not a regular file -- skipping", file) else: dest = os.path.join(base_dir, file) self.copy_file(file, dest, link=link) self.distribution.metadata.write_pkg_info(base_dir)

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

XX-Net

Python

8198943edd73a363c266633e1aa5b2a9e9c9f526

sdist.py

make_release_tree

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

add python 3.10.4 for windows

def run_after_hook(self): return None

wagtail/admin/views/generic/mixins.py

wagtail

Python

bc1a2ab1148b0f27cfd1435f8cb0e44c2721102d

mixins.py

run_after_hook

https://github.com/wagtail/wagtail.git

Extract mixins from Snippet views and use it in generic create/edit/delete views (#8361)

def __setitem__(self, name, val): max_count = self.policy.header_max_count(name) if max_count: lname = name.lower() found = 0 for k, v in self._headers: if k.lower() == lname: found += 1 if found >= max_count: raise ValueError("There may be at most {} {} headers " "in a message".format(max_count, name)) self._headers.append(self.policy.header_store_parse(name, val))

python3.10.4/Lib/email/message.py

XX-Net

Python

8198943edd73a363c266633e1aa5b2a9e9c9f526

message.py

__setitem__

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

add python 3.10.4 for windows

def __call__(self, *i): # list indices can be Integer or int; leave this # as it is (don't test or convert it) because this # gets called a lot and should be fast if len(i) == 1: i = i[0] if not isinstance(i, Iterable): i = as_int(i) if i < 0 or i > self.size: raise TypeError( "{} should be an integer between 0 and {}" .format(i, self.size-1)) return self._array_form[i] # P([a, b, c]) if len(i) != self.size: raise TypeError( "{} should have the length {}.".format(i, self.size)) return [i[j] for j in self._array_form] # P(1, 2, 3) return self*Permutation(Cycle(*i), size=self.size)

sympy/combinatorics/permutations.py

sympy

Python

498015021131af4dbb07eb110e5badaba8250c7b

permutations.py

__call__

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

Updated import locations

def transform(self, X): check_is_fitted(self) X = self._validate_input(X, in_fit=False) statistics = self.statistics_ if X.shape[1] != statistics.shape[0]: raise ValueError( "X has %d features per sample, expected %d" % (X.shape[1], self.statistics_.shape[0]) ) # compute mask before eliminating invalid features missing_mask = _get_mask(X, self.missing_values) # Decide whether to keep missing features if self.strategy == "constant" or self.keep_empty_features: valid_statistics = statistics valid_statistics_indexes = None else: # same as np.isnan but also works for object dtypes invalid_mask = _get_mask(statistics, np.nan) valid_mask = np.logical_not(invalid_mask) valid_statistics = statistics[valid_mask] valid_statistics_indexes = np.flatnonzero(valid_mask) if invalid_mask.any(): invalid_features = np.arange(X.shape[1])[invalid_mask] if self.verbose != "deprecated" and self.verbose: # use feature names warning if features are provided if hasattr(self, "feature_names_in_"): invalid_features = self.feature_names_in_[invalid_features] warnings.warn( "Skipping features without any observed values:" f" {invalid_features}. At least one non-missing value is needed" f" for imputation with strategy='{self.strategy}'." ) X = X[:, valid_statistics_indexes] # Do actual imputation if sp.issparse(X): if self.missing_values == 0: raise ValueError( "Imputation not possible when missing_values " "== 0 and input is sparse. Provide a dense " "array instead." ) else: # if no invalid statistics are found, use the mask computed # before, else recompute mask if valid_statistics_indexes is None: mask = missing_mask.data else: mask = _get_mask(X.data, self.missing_values) indexes = np.repeat( np.arange(len(X.indptr) - 1, dtype=int), np.diff(X.indptr) )[mask] X.data[mask] = valid_statistics[indexes].astype(X.dtype, copy=False) else: # use mask computed before eliminating invalid mask if valid_statistics_indexes is None: mask_valid_features = missing_mask else: mask_valid_features = missing_mask[:, valid_statistics_indexes] n_missing = np.sum(mask_valid_features, axis=0) values = np.repeat(valid_statistics, n_missing) coordinates = np.where(mask_valid_features.transpose())[::-1] X[coordinates] = values X_indicator = super()._transform_indicator(missing_mask) return super()._concatenate_indicator(X, X_indicator)

sklearn/impute/_base.py

scikit-learn

Python

d8fa96c29828e3ca79ddd5d7466521ac4d95213c

_base.py

transform

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

ENH keep features with all missing values during imputation (#24770) Co-authored-by: Chiara Marmo <[email protected]> Co-authored-by: Julien Jerphanion <[email protected]> Co-authored-by: Jérémie du Boisberranger <[email protected]> Co-authored-by: Vitor SRG <[email protected]> Fixes https://github.com/scikit-learn/scikit-learn/pull/16695 Fixes https://github.com/scikit-learn/scikit-learn/issues/16426 Fixes https://github.com/scikit-learn/scikit-learn/issues/16977

def get_running_loop(): # NOTE: this function is implemented in C (see _asynciomodule.c) loop = _get_running_loop() if loop is None: raise RuntimeError('no running event loop') return loop

python3.10.4/Lib/asyncio/events.py

XX-Net

Python

8198943edd73a363c266633e1aa5b2a9e9c9f526

events.py

get_running_loop

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

add python 3.10.4 for windows

def _predict_faces(self) -> None: faces_seen = 0 consecutive_no_faces = 0 batch: List[ConvertItem] = [] is_amd = get_backend() == "amd" while True: item: Union[Literal["EOF"], ConvertItem] = self._in_queue.get() if item == "EOF": logger.debug("EOF Received") break logger.trace("Got from queue: '%s'", item.inbound.filename) # type:ignore faces_count = len(item.inbound.detected_faces) # Safety measure. If a large stream of frames appear that do not have faces, # these will stack up into RAM. Keep a count of consecutive frames with no faces. # If self._batchsize number of frames appear, force the current batch through # to clear RAM. consecutive_no_faces = consecutive_no_faces + 1 if faces_count == 0 else 0 self._faces_count += faces_count if faces_count > 1: self._verify_output = True logger.verbose("Found more than one face in an image! '%s'", # type:ignore os.path.basename(item.inbound.filename)) self.load_aligned(item) faces_seen += faces_count batch.append(item) if faces_seen < self._batchsize and consecutive_no_faces < self._batchsize: logger.trace("Continuing. Current batchsize: %s, " # type:ignore "consecutive_no_faces: %s", faces_seen, consecutive_no_faces) continue if batch: logger.trace("Batching to predictor. Frames: %s, Faces: %s", # type:ignore len(batch), faces_seen) feed_batch = [feed_face for item in batch for feed_face in item.feed_faces] if faces_seen != 0: feed_faces = self._compile_feed_faces(feed_batch) batch_size = None if is_amd and feed_faces.shape[0] != self._batchsize: logger.verbose("Fallback to BS=1") # type:ignore batch_size = 1 predicted = self._predict(feed_faces, batch_size) else: predicted = np.array([]) self._queue_out_frames(batch, predicted) consecutive_no_faces = 0 faces_seen = 0 batch = [] logger.debug("Putting EOF") self._out_queue.put("EOF") logger.debug("Load queue complete")

scripts/convert.py

faceswap

Python

1022651eb8a7741014f5d2ec7cbfe882120dfa5f

convert.py

_predict_faces

https://github.com/deepfakes/faceswap.git

Bugfix: convert - Gif Writer - Fix non-launch error on Gif Writer - convert plugins - linting - convert/fs_media/preview/queue_manager - typing - Change convert items from dict to Dataclass

def prepare(self, timestamp, duration, organization): reports = {} for project in organization.project_set.all(): reports[project.id] = self.__encode(self.build(timestamp, duration, project)) if not reports: # XXX: HMSET requires at least one key/value pair, so we need to # protect ourselves here against organizations that were created # but haven't set up any projects yet. return with self.cluster.map() as client: key = self.__make_key(timestamp, duration, organization) client.hmset(key, reports) client.expire(key, self.ttl)

src/sentry/tasks/reports.py

sentry

Python

9731253cf103cfdced62c36753a0e957ab29d705

reports.py

prepare

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

feat: Add instrumentation to Celery tasks for weekly reports (#38561) It seems that if we don't include the parent celery task, it will not trace any of the children tasks. This enables further investigation as to why the building of the report is slow. Fixes WOR-2159.

def test_file_not_found_error(self): response = self.get_success_response( self.organization.slug, self.project.slug, qs_params={"file": self.filepath} ) assert response.data["config"] == self.expected_configurations(self.code_mapping1) assert not response.data["sourceUrl"] # XXX: This depends on what was the last attempted code mapping assert response.data["error"] == "stack_root_mismatch" assert response.data["integrations"] == [serialized_integration(self.integration)] # XXX: This depends on what was the last attempted code mapping assert ( response.data["attemptedUrl"] == f"https://example.com/{self.repo.name}/blob/master/src/sentry/src/sentry/utils/safe.py" )

tests/sentry/api/endpoints/test_project_stacktrace_link.py

sentry

Python

2e0d2c856eb17a842c67d88363bed92c99578c20

test_project_stacktrace_link.py

test_file_not_found_error

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

ref(stacktrace_link): Add more than one code mapping in the tests (#41409) Include more than one code mapping in the setup code. Cleaning up a bit how we tag the transactions. This makes the PR for WOR-2395 a little easier to read.

def has_pretrained_cfg_key(model_name, cfg_key): if model_name in _model_pretrained_cfgs and cfg_key in _model_pretrained_cfgs[model_name]: return True return False

timm/models/registry.py

pytorch-image-models

Python

abc9ba254430ef971ea3dbd12f2b4f1969da55be

registry.py

has_pretrained_cfg_key

https://github.com/huggingface/pytorch-image-models.git

Transitioning default_cfg -> pretrained_cfg. Improving handling of pretrained_cfg source (HF-Hub, files, timm config, etc). Checkpoint handling tweaks.

def is_ccl_available(): return _is_ccl_available # docstyle-ignore DATASETS_IMPORT_ERROR = # docstyle-ignore TOKENIZERS_IMPORT_ERROR = # docstyle-ignore SENTENCEPIECE_IMPORT_ERROR = # docstyle-ignore PROTOBUF_IMPORT_ERROR = # docstyle-ignore FAISS_IMPORT_ERROR = # docstyle-ignore PYTORCH_IMPORT_ERROR = # docstyle-ignore PYTORCH_IMPORT_ERROR_WITH_TF = # docstyle-ignore TF_IMPORT_ERROR_WITH_PYTORCH = # docstyle-ignore SKLEARN_IMPORT_ERROR = # docstyle-ignore TENSORFLOW_IMPORT_ERROR = # docstyle-ignore DETECTRON2_IMPORT_ERROR = # docstyle-ignore FLAX_IMPORT_ERROR = # docstyle-ignore FTFY_IMPORT_ERROR = # docstyle-ignore SCATTER_IMPORT_ERROR = # docstyle-ignore PYTORCH_QUANTIZATION_IMPORT_ERROR = # docstyle-ignore TENSORFLOW_PROBABILITY_IMPORT_ERROR = # docstyle-ignore TENSORFLOW_TEXT_IMPORT_ERROR = # docstyle-ignore PANDAS_IMPORT_ERROR = # docstyle-ignore PHONEMIZER_IMPORT_ERROR = # docstyle-ignore SACREMOSES_IMPORT_ERROR = # docstyle-ignore SCIPY_IMPORT_ERROR = # docstyle-ignore SPEECH_IMPORT_ERROR = # docstyle-ignore TIMM_IMPORT_ERROR = # docstyle-ignore VISION_IMPORT_ERROR = # docstyle-ignore PYTESSERACT_IMPORT_ERROR = # docstyle-ignore PYCTCDECODE_IMPORT_ERROR = # docstyle-ignore ACCELERATE_IMPORT_ERROR = # docstyle-ignore CCL_IMPORT_ERROR = BACKENDS_MAPPING = OrderedDict( [ ("datasets", (is_datasets_available, DATASETS_IMPORT_ERROR)), ("detectron2", (is_detectron2_available, DETECTRON2_IMPORT_ERROR)), ("faiss", (is_faiss_available, FAISS_IMPORT_ERROR)), ("flax", (is_flax_available, FLAX_IMPORT_ERROR)), ("ftfy", (is_ftfy_available, FTFY_IMPORT_ERROR)), ("pandas", (is_pandas_available, PANDAS_IMPORT_ERROR)), ("phonemizer", (is_phonemizer_available, PHONEMIZER_IMPORT_ERROR)), ("protobuf", (is_protobuf_available, PROTOBUF_IMPORT_ERROR)), ("pyctcdecode", (is_pyctcdecode_available, PYCTCDECODE_IMPORT_ERROR)), ("pytesseract", (is_pytesseract_available, PYTESSERACT_IMPORT_ERROR)), ("sacremoses", (is_sacremoses_available, SACREMOSES_IMPORT_ERROR)), ("scatter", (is_scatter_available, SCATTER_IMPORT_ERROR)), ("pytorch_quantization", (is_pytorch_quantization_available, PYTORCH_QUANTIZATION_IMPORT_ERROR)), ("sentencepiece", (is_sentencepiece_available, SENTENCEPIECE_IMPORT_ERROR)), ("sklearn", (is_sklearn_available, SKLEARN_IMPORT_ERROR)), ("speech", (is_speech_available, SPEECH_IMPORT_ERROR)), ("tensorflow_probability", (is_tensorflow_probability_available, TENSORFLOW_PROBABILITY_IMPORT_ERROR)), ("tf", (is_tf_available, TENSORFLOW_IMPORT_ERROR)), ("tensorflow_text", (is_tensorflow_text_available, TENSORFLOW_TEXT_IMPORT_ERROR)), ("timm", (is_timm_available, TIMM_IMPORT_ERROR)), ("tokenizers", (is_tokenizers_available, TOKENIZERS_IMPORT_ERROR)), ("torch", (is_torch_available, PYTORCH_IMPORT_ERROR)), ("vision", (is_vision_available, VISION_IMPORT_ERROR)), ("scipy", (is_scipy_available, SCIPY_IMPORT_ERROR)), ("accelerate", (is_accelerate_available, ACCELERATE_IMPORT_ERROR)), ("oneccl_bind_pt", (is_ccl_available, CCL_IMPORT_ERROR)), ] )

src/transformers/utils/import_utils.py

transformers

Python

2b81f72be9fa6d69734ae27cfcbfd72b04988fe4

import_utils.py

is_ccl_available

https://github.com/huggingface/transformers.git

start from 1.12, torch_ccl is renamed as oneccl_bindings_for_pytorch … (#18229) * start from 1.12, torch_ccl is renamed as oneccl_bindings_for_pytorch and should import it before use Signed-off-by: Wang, Yi A <[email protected]> * add doc for perf_train_cpu_many Signed-off-by: Wang, Yi A <[email protected]> * update doc Signed-off-by: Wang, Yi A <[email protected]>

def test_json_get_subscribers(self) -> None: stream_name = gather_subscriptions(self.user_profile)[0][0]["name"] stream_id = get_stream(stream_name, self.user_profile.realm).id expected_subscribers = gather_subscriptions(self.user_profile, include_subscribers=True)[0][ 0 ]["subscribers"] result = self.client_get(f"/json/streams/{stream_id}/members") result_dict = self.assert_json_success(result) self.assertIn("subscribers", result_dict) self.assertIsInstance(result_dict["subscribers"], list) subscribers: List[int] = [] for subscriber in result_dict["subscribers"]: self.assertIsInstance(subscriber, int) subscribers.append(subscriber) self.assertEqual(set(subscribers), set(expected_subscribers))

zerver/tests/test_subs.py

zulip

Python

a142fbff85302c5e3acb2e204eca2e9c75dbc74b

test_subs.py

test_json_get_subscribers

https://github.com/zulip/zulip.git

tests: Refactor away result.json() calls with helpers. Signed-off-by: Zixuan James Li <[email protected]>

def _fix_compile_args(self, output_dir, macros, include_dirs): if output_dir is None: output_dir = self.output_dir elif not isinstance(output_dir, str): raise TypeError("'output_dir' must be a string or None") if macros is None: macros = self.macros elif isinstance(macros, list): macros = macros + (self.macros or []) else: raise TypeError("'macros' (if supplied) must be a list of tuples") if include_dirs is None: include_dirs = self.include_dirs elif isinstance(include_dirs, (list, tuple)): include_dirs = list(include_dirs) + (self.include_dirs or []) else: raise TypeError( "'include_dirs' (if supplied) must be a list of strings") return output_dir, macros, include_dirs

python3.10.4/Lib/distutils/ccompiler.py

XX-Net

Python

8198943edd73a363c266633e1aa5b2a9e9c9f526

ccompiler.py

_fix_compile_args

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

add python 3.10.4 for windows

def get_template_names(self): try: names = super().get_template_names() except ImproperlyConfigured: # If template_name isn't specified, it's not a problem -- # we just start with an empty list. names = [] # If the list is a queryset, we'll invent a template name based on the # app and model name. This name gets put at the end of the template # name list so that user-supplied names override the automatically- # generated ones. if hasattr(self.object_list, "model"): opts = self.object_list.model._meta names.append( "%s/%s%s.html" % (opts.app_label, opts.model_name, self.template_name_suffix) ) elif not names: raise ImproperlyConfigured( "%(cls)s requires either a 'template_name' attribute " "or a get_queryset() method that returns a QuerySet." % { "cls": self.__class__.__name__, } ) return names

django/views/generic/list.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

list.py

get_template_names

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

Refs #33476 -- Reformatted code with Black.

def _SpinboxSelectHandler(self): self._generic_callback_handler('')

PySimpleGUI.py

PySimpleGUI

Python

40757180b5d0ac66d44958e4ab13329c7b03ea36

PySimpleGUI.py

_SpinboxSelectHandler

https://github.com/PySimpleGUI/PySimpleGUI.git

Fix for enable_events for Spin element. Changed how the event is generated. Need to determine manual entry of value still

def test_small_integration_test(self): model = TFAutoModelForSeq2SeqLM.from_pretrained("google/mt5-small") tokenizer = AutoTokenizer.from_pretrained("google/mt5-small") input_ids = tokenizer("Hello there", return_tensors="tf").input_ids labels = tokenizer("Hi I am", return_tensors="tf").input_ids loss = model(input_ids, labels=labels).loss mtf_score = -tf.math.reduce_mean(loss).numpy() EXPECTED_SCORE = -21.210594 self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 2e-4)

tests/models/mt5/test_modeling_tf_mt5.py

transformers

Python

5ae087cf8ec080b121c9cdc9bafdc2b35b6e110e

test_modeling_tf_mt5.py

test_small_integration_test

https://github.com/huggingface/transformers.git

Fix T5/mT5 tests (#18029)

async def test_in_interface(self): iface = gr.Interface(lambda x: x, "text", "markdown") input_data = "Here's an [image](https://gradio.app/images/gradio_logo.png)" output_data = iface(input_data) assert ( output_data == )

test/test_components.py

gradio

Python

d79039beb1c3eab597de4871f7eb6522196d1a00

test_components.py

test_in_interface

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

Latex support (#2696) * initial use of dollarmath plugin * add frontend support * chnages * changes * changes * changes * changes * fix * added latex to kinematics blocks * changes * Update CHANGELOG.md Co-authored-by: Abubakar Abid <[email protected]> * added example to changelog * remove param * doc fix * fixes * latex noteboox fix * fix * changes Co-authored-by: Ali Abid <[email protected]> Co-authored-by: Abubakar Abid <[email protected]>

def gelu(x): return x * 0.5 * (1.0 + paddle.erf(x / math.sqrt(2.0)))

modules/image/text_to_image/disco_diffusion_cnclip_vitb16/cn_clip/clip/modeling_bert.py

PaddleHub

Python

f4d6e64cdc132ae868699a0ba442f4ab1d304a14

modeling_bert.py

gelu

https://github.com/PaddlePaddle/PaddleHub.git

add disco_diffusion_cnclip_vitb16 module

def _set_preview_feed(self): retval = {} for idx, side in enumerate(("a", "b")): logger.debug("Setting preview feed: (side: '%s')", side) preview_images = self._config.get("preview_images", 14) preview_images = min(max(preview_images, 2), 16) batchsize = min(len(self._images[side]), preview_images) retval[side] = self._load_generator(idx).minibatch_ab(self._images[side], batchsize, side, do_shuffle=True, is_preview=True) logger.debug("Set preview feed. Batchsize: %s", batchsize) return retval

plugins/train/trainer/_base.py

faceswap

Python

c1512fd41d86ef47a5d1ce618d6d755ef7cbacdf

_base.py

_set_preview_feed

https://github.com/deepfakes/faceswap.git

Update code to support Tensorflow versions up to 2.8 (#1213) * Update maximum tf version in setup + requirements * - bump max version of tf version in launcher - standardise tf version check * update keras get_custom_objects for tf>2.6 * bugfix: force black text in GUI file dialogs (linux) * dssim loss - Move to stock tf.ssim function * Update optimizer imports for compatibility * fix logging for tf2.8 * Fix GUI graphing for TF2.8 * update tests * bump requirements.txt versions * Remove limit on nvidia-ml-py * Graphing bugfixes - Prevent live graph from displaying if data not yet available * bugfix: Live graph. Collect loss labels correctly * fix: live graph - swallow inconsistent loss errors * Bugfix: Prevent live graph from clearing during training * Fix graphing for AMD

def script_error_handler(path, exc, msg="", tb=False): exception = type(exc).__name__ if msg: exception = msg lineno = "" if hasattr(exc, "lineno"): lineno = str(exc.lineno) log_msg = f"in script {path}:{lineno} {exception}" if tb: etype, value, tback = sys.exc_info() tback = addonmanager.cut_traceback(tback, "invoke_addon_sync") log_msg = ( log_msg + "\n" + "".join(traceback.format_exception(etype, value, tback)) ) ctx.log.error(log_msg) ReloadInterval = 1

mitmproxy/addons/script.py

mitmproxy

Python

b3587b52b25077f68116b9852b041d33e7fc6601

script.py

script_error_handler

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

make it black!

def prevent_sync_event_circular_query(func):

saleor/graphql/checkout/utils.py

saleor

Python

8201efcde2d7aacccf3512c544cceea6780a0598

utils.py

prevent_sync_event_circular_query

https://github.com/saleor/saleor.git

GraphQL subscription support for synchronous webhook events (#9763) * WIP add sync webhooks subscription payload handling * add tests, fix minor things * update schema * remove unneeded code * add fix for circular field resolve * fix-filter-shipping-methods-payload * added_in added to desription * add missing types * revert refactor, precommit issues * fixes after review * cosmetix fixes post-review * subscription types description fixes * remove unneeded description from PaymentBase * add validation for creating webhook with two top level fields, add tests for shippingListMethodsForCheckout * add docstring, refactor prevent_sync_event_circular_wuery wrapper * fix docstring of revent_sync_event_circular_query * fix linters

def iterations(self): return self._iterations

keras/optimizers/optimizer_experimental/optimizer.py

keras

Python

84afc5193d38057e2e2badf9c889ea87d80d8fbf

optimizer.py

iterations

https://github.com/keras-team/keras.git

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

def test_nested_auto_heights(snap_compare): assert snap_compare("snapshot_apps/nested_auto_heights.py", press=["1", "2"]) # --- Other ---

tests/snapshot_tests/test_snapshots.py

textual

Python

32b7308ac83c20c49ca422726be149fdc5b8fc2d

test_snapshots.py

test_nested_auto_heights

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

fox for nested heights

def num_arrays_on_dev(dev): return len(get_all_arrays_on_dev(dev)) # noinspection PyShadowingNames

ivy/core/device.py

ivy

Python

d743336b1f3654cd0315f380f43eed4116997c1d

device.py

num_arrays_on_dev

https://github.com/unifyai/ivy.git

renamed dev_str arg to dev for all methods.

def pattern(self) -> str | None: if hasattr(self, "_attr_pattern"): return self._attr_pattern if hasattr(self, "entity_description"): return self.entity_description.pattern return None

homeassistant/components/text/__init__.py

core

Python

003e4224c89a6da381960dc5347750d1521d85c9

__init__.py

pattern

https://github.com/home-assistant/core.git

Add `text` platform (#79454) Co-authored-by: Franck Nijhof <[email protected]> Co-authored-by: Franck Nijhof <[email protected]>

def render_pep440(pieces): if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += plus_or_dot(pieces) rendered += f"{pieces['distance']}.g{pieces['short']}" if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = f"0+untagged.{pieces['distance']}.g{pieces['short']}" if pieces["dirty"]: rendered += ".dirty" return rendered

pandas/_version.py

pandas

Python

e2df99823758210fb2b7c4aba39e23f3445f7cd3

_version.py

render_pep440

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

BLD: use nonvendor versioneer (#49924) * BLD: remove vendored versioneer * run vis * move config to pyproject.toml * add versioneer to deps * run pyupgrade * fix isort and pylint * fix ci * fix env

def clear(self): del self._toklist[:] self._tokdict.clear()

pipenv/patched/notpip/_vendor/pyparsing/results.py

pipenv

Python

f3166e673fe8d40277b804d35d77dcdb760fc3b3

results.py

clear

https://github.com/pypa/pipenv.git

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

def _refresh_on_access_denied(func):

homeassistant/components/ubus/device_tracker.py

core

Python

8819634b613f6bfd55885283bab86c3852ae40c4

device_tracker.py

_refresh_on_access_denied

https://github.com/home-assistant/core.git

String formatting and max line length - Part 6 (#84525)

def format(tokens, formatter, outfile=None): # pylint: disable=redefined-builtin try: if not outfile: realoutfile = getattr(formatter, 'encoding', None) and BytesIO() or StringIO() formatter.format(tokens, realoutfile) return realoutfile.getvalue() else: formatter.format(tokens, outfile) except TypeError as err: if (isinstance(err.args[0], str) and ('unbound method format' in err.args[0] or 'missing 1 required positional argument' in err.args[0])): raise TypeError('format() argument must be a formatter instance, ' 'not a class') raise

pipenv/patched/notpip/_vendor/pygments/__init__.py

pipenv

Python

f3166e673fe8d40277b804d35d77dcdb760fc3b3

__init__.py

format

https://github.com/pypa/pipenv.git

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

def get_bin_path(arg, opt_dirs=None, required=None): opt_dirs = [] if opt_dirs is None else opt_dirs sbin_paths = ['/sbin', '/usr/sbin', '/usr/local/sbin'] paths = [] for d in opt_dirs: if d is not None and os.path.exists(d): paths.append(d) paths += os.environ.get('PATH', '').split(os.pathsep) bin_path = None # mangle PATH to include /sbin dirs for p in sbin_paths: if p not in paths and os.path.exists(p): paths.append(p) for d in paths: if not d: continue path = os.path.join(d, arg) if os.path.exists(path) and not os.path.isdir(path) and is_executable(path): bin_path = path break if bin_path is None: raise ValueError('Failed to find required executable "%s" in paths: %s' % (arg, os.pathsep.join(paths))) return bin_path

lib/ansible/module_utils/common/process.py

ansible

Python

b56d73796e85f162d50b4fcd5930035183032d4a

process.py

get_bin_path

https://github.com/ansible/ansible.git

Clarify that sbin directories are always looked at in get_bin_path (#78171)

def aggregate(self, *args, **kwargs): if self.query.distinct_fields: raise NotImplementedError("aggregate() + distinct(fields) not implemented.") self._validate_values_are_expressions( (*args, *kwargs.values()), method_name="aggregate" ) for arg in args: # The default_alias property raises TypeError if default_alias # can't be set automatically or AttributeError if it isn't an # attribute. try: arg.default_alias except (AttributeError, TypeError): raise TypeError("Complex aggregates require an alias") kwargs[arg.default_alias] = arg query = self.query.chain() for (alias, aggregate_expr) in kwargs.items(): query.add_annotation(aggregate_expr, alias, is_summary=True) annotation = query.annotations[alias] if not annotation.contains_aggregate: raise TypeError("%s is not an aggregate expression" % alias) for expr in annotation.get_source_expressions(): if ( expr.contains_aggregate and isinstance(expr, Ref) and expr.refs in kwargs ): name = expr.refs raise exceptions.FieldError( "Cannot compute %s('%s'): '%s' is an aggregate" % (annotation.name, name, name) ) return query.get_aggregation(self.db, kwargs)

django/db/models/query.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

query.py

aggregate

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

Refs #33476 -- Reformatted code with Black.

def to_local_object_without_private_data_child(self) -> NDimEntityPhiTensor: # relative from ..tensor import Tensor public_shape = getattr(self, "public_shape", None) public_dtype = getattr(self, "public_dtype", None) return Tensor( child=NDimEntityPhiTensor( child=FixedPrecisionTensor(value=None), entities=self.entities, min_vals=self.min_vals, # type: ignore max_vals=self.max_vals, # type: ignore ), public_shape=public_shape, public_dtype=public_dtype, ) @serializable(capnp_bytes=True)

packages/syft/src/syft/core/tensor/autodp/ndim_entity_phi.py

@serializable(capnp_bytes=True)

PySyft

Python

8fdb37e3227eb40d431c32ae8f5bfb44866e4490

ndim_entity_phi.py

to_local_object_without_private_data_child

https://github.com/OpenMined/PySyft.git

working ndept addition

def current_umask() -> int: mask = os.umask(0) os.umask(mask) return mask

pipenv/patched/notpip/_internal/utils/unpacking.py

pipenv

Python

f3166e673fe8d40277b804d35d77dcdb760fc3b3

unpacking.py

current_umask

https://github.com/pypa/pipenv.git

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

async def _recover_running_jobs(self): all_jobs = await self._job_info_client.get_all_jobs() for job_id, job_info in all_jobs.items(): if not job_info.status.is_terminal(): create_task(self._monitor_job(job_id))

dashboard/modules/job/job_manager.py

ray

Python

326b5bd1acc6d3d00ab0546e4ae45da6bed501f7

job_manager.py

_recover_running_jobs

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

Convert job_manager to be async (#27123) Updates jobs api Updates snapshot api Updates state api Increases jobs api version to 2 Signed-off-by: Alan Guo [email protected] Why are these changes needed? follow-up for #25902 (comment)

def collate_full_clips(batch): max_mel_length = max([b[0].shape[1] for b in batch]) if len(batch) > 1 else batch[0][0].shape[1] max_audio_length = max([b[1].shape[0] for b in batch]) if len(batch) > 1 else batch[0][1].shape[0] mels = torch.zeros([len(batch), batch[0][0].shape[0], max_mel_length]) audios = torch.zeros([len(batch), max_audio_length]) for idx, b in enumerate(batch): mel = b[0] audio = b[1] mels[idx, :, : mel.shape[1]] = mel audios[idx, : audio.shape[0]] = audio return mels, audios

TTS/vocoder/datasets/wavegrad_dataset.py

TTS

Python

2c9f00a808e0aa76a82af2e8b325abb71f50d1df

wavegrad_dataset.py

collate_full_clips

https://github.com/coqui-ai/TTS.git

Fix tune wavegrad (#1844) * fix imports in tune_wavegrad * load_config returns Coqpit object instead None * set action (store true) for flag "--use_cuda"; start to tune if module is running as the main program * fix var order in the result of batch collating * make style * make style with black and isort

def waist2rayleigh(w, wavelen, n=1): w, wavelen = map(sympify, (w, wavelen)) return w**2*n*pi/wavelen

sympy/physics/optics/gaussopt.py

sympy

Python

c32aa66c02befb7a12915e6ae4ae953a1a81c8f7

gaussopt.py

waist2rayleigh

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

Refractive_Index_Parameter_Considered

def __copy__(self): # Shallow copy. return self.__constructor__( self.gpu_manager, self.key, self._length_cache, self._width_cache )

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

modin

Python

2bb9a1fab7b0092974853e616dfd5e7ed98f085d

partition.py

__copy__

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

REFACTOR-#5363: introduce partition constructor; move `add_to_apply_calls` impl in base class (#5354) Signed-off-by: Myachev <[email protected]>

def upgrade(): try: with op.batch_alter_table('connection') as batch_op: batch_op.alter_column("conn_id", nullable=False, existing_type=sa.String(250, **COLLATION_ARGS)) batch_op.create_unique_constraint(constraint_name="unique_conn_id", columns=["conn_id"]) except sa.exc.IntegrityError: raise Exception("Make sure there are no duplicate connections with the same conn_id or null values")

airflow/migrations/versions/8d48763f6d53_add_unique_constraint_to_conn_id.py

airflow

Python

69f6f9e01b6df76c3c8fa266d460324163957887

8d48763f6d53_add_unique_constraint_to_conn_id.py

upgrade

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

Autogenerate migration reference doc (#21601) * document airflow version in each alembic migration module and use this to autogen the doc * update each migration module to have the same description used in migration ref (so it can be used in autogen)

def forward(self, input_ids, token_type_ids=None, attention_mask=None): r if attention_mask is None: attention_mask = paddle.unsqueeze( (input_ids == self.pad_token_id ).astype(self.pooler.dense.weight.dtype) * -1e4, axis=[1, 2]) embedding_output = self.embeddings(input_ids, token_type_ids) encoded_layer = self.encoder(embedding_output, attention_mask) pooled_output = self.pooler(encoded_layer) return encoded_layer, pooled_output

paddlenlp/transformers/tinybert/modeling.py

PaddleNLP

Python

b0c35d5e1ff02a634fa26392b60d3885c2c78677

modeling.py

forward

https://github.com/PaddlePaddle/PaddleNLP.git

Fix the attention mask for fp16 (#1585)

def available(self) -> bool: return self.netdata.available

homeassistant/components/netdata/sensor.py

core

Python

420733a064286cfe6fc5cf11483835d15ff83462

sensor.py

available

https://github.com/home-assistant/core.git

Improve entity type hints [n] (#77824)

def vlatex(expr, **settings): r latex_printer = VectorLatexPrinter(settings) return latex_printer.doprint(expr)

sympy/physics/vector/printing.py

sympy

Python

9a3ffc6781bd44c47cf49e128ef154389c32876a

printing.py

vlatex

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

Some pep8 cleanup of sympy.physics.vector.

def test_pick_two_individuals_eligible_for_crossover_bad(): ind1 = creator.Individual.from_string( 'BernoulliNB(input_matrix, BernoulliNB__alpha=1.0, BernoulliNB__fit_prior=True)', tpot_obj._pset ) ind2 = creator.Individual.from_string( 'BernoulliNB(input_matrix, BernoulliNB__alpha=1.0, BernoulliNB__fit_prior=True)', tpot_obj._pset ) ind3 = creator.Individual.from_string( 'GaussianNB(input_matrix)', tpot_obj._pset ) # Ind1 and ind2 are not a pair because they are the same, ind3 shares no primitive pick1, pick2 = pick_two_individuals_eligible_for_crossover([ind1, ind2, ind3]) assert pick1 is None and pick2 is None # You can not do crossover with a population of only 1. pick1, pick2 = pick_two_individuals_eligible_for_crossover([ind1]) assert pick1 is None and pick2 is None # You can not do crossover with a population of 0. pick1, pick2 = pick_two_individuals_eligible_for_crossover([]) assert pick1 is None and pick2 is None

tests/tpot_tests.py

tpot

Python

388616b6247ca4ea8de4e2f340d6206aee523541

tpot_tests.py

test_pick_two_individuals_eligible_for_crossover_bad

https://github.com/EpistasisLab/tpot.git

Revert "Deployed 7ccda9a with MkDocs version: 1.3.0" This reverts commit bd9629c40e01241766197119b581a99409b07068.

def _vindex(x, *indexes): indexes = replace_ellipsis(x.ndim, indexes) nonfancy_indexes = [] reduced_indexes = [] for ind in indexes: if isinstance(ind, Number): nonfancy_indexes.append(ind) elif isinstance(ind, slice): nonfancy_indexes.append(ind) reduced_indexes.append(slice(None)) else: nonfancy_indexes.append(slice(None)) reduced_indexes.append(ind) nonfancy_indexes = tuple(nonfancy_indexes) reduced_indexes = tuple(reduced_indexes) x = x[nonfancy_indexes] array_indexes = {} for i, (ind, size) in enumerate(zip(reduced_indexes, x.shape)): if not isinstance(ind, slice): ind = np.array(ind, copy=True) if ind.dtype.kind == "b": raise IndexError("vindex does not support indexing with boolean arrays") if ((ind >= size) | (ind < -size)).any(): raise IndexError( "vindex key has entries out of bounds for " "indexing along axis %s of size %s: %r" % (i, size, ind) ) ind %= size array_indexes[i] = ind if array_indexes: x = _vindex_array(x, array_indexes) return x

dask/array/core.py

dask

Python

b016998fa931f644df4d266a3ed5e7604c20d2a9

core.py

_vindex

https://github.com/dask/dask.git

Removed unused loop control variables (`B007`) (#9458) Co-authored-by: James Bourbeau <[email protected]>

def test_warn_once(): with warnings.catch_warnings(record=True) as record: # Ignore Deprecation warnings. warnings.filterwarnings("ignore", category=DeprecationWarning) assert not load_checkpoint() assert not load_checkpoint() assert not save_checkpoint(x=2) assert not report(x=2) assert not report(x=3) assert not get_dataset_shard() # Should only warn once. assert len(record) == 4

python/ray/train/tests/test_session.py

ray

Python

0e8eb8aedb3e158da8c3e7378e818ce87ca7813e

test_session.py

test_warn_once

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

[AIR] More Train and Tune session deprecations (#28856) Signed-off-by: Amog Kamsetty [email protected] Finish marking train. and tune. session APIs as deprecated

def _tie_weights(self): # To tie those two weights if they get disconnected (on TPU or when the bias is resized) self.bias = self.decoder.bias @add_start_docstrings( , XLM_ROBERTA_XL_START_DOCSTRING, )

src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py

@add_start_docstrings( """ XLM-RoBERTa-xlarge Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, XLM_ROBERTA_XL_START_DOCSTRING, )

transformers

Python

e09473a817c5e5871e11cc81004355ef30250502

modeling_xlm_roberta_xl.py

_tie_weights

https://github.com/huggingface/transformers.git

Add support for XLM-R XL and XXL models by modeling_xlm_roberta_xl.py (#13727) * add xlm roberta xl * add convert xlm xl fairseq checkpoint to pytorch * fix init and documents for xlm-roberta-xl * fix indention * add test for XLM-R xl,xxl * fix model hub name * fix some stuff * up * correct init * fix more * fix as suggestions * add torch_device * fix default values of doc strings * fix leftovers * merge to master * up * correct hub names * fix docs * fix model * up * finalize * last fix * Apply suggestions from code review Co-authored-by: Sylvain Gugger <[email protected]> * add copied from * make style Co-authored-by: Patrick von Platen <[email protected]> Co-authored-by: Sylvain Gugger <[email protected]>

def _convert_mesh_to_triangles(self, coordinates): if isinstance(coordinates, np.ma.MaskedArray): p = coordinates.data else: p = coordinates p_a = p[:-1, :-1] p_b = p[:-1, 1:] p_c = p[1:, 1:] p_d = p[1:, :-1] p_center = (p_a + p_b + p_c + p_d) / 4.0 triangles = np.concatenate([ p_a, p_b, p_center, p_b, p_c, p_center, p_c, p_d, p_center, p_d, p_a, p_center, ], axis=2).reshape((-1, 3, 2)) c = self.get_facecolor().reshape((*coordinates.shape[:2], 4)) c_a = c[:-1, :-1] c_b = c[:-1, 1:] c_c = c[1:, 1:] c_d = c[1:, :-1] c_center = (c_a + c_b + c_c + c_d) / 4.0 colors = np.concatenate([ c_a, c_b, c_center, c_b, c_c, c_center, c_c, c_d, c_center, c_d, c_a, c_center, ], axis=2).reshape((-1, 3, 4)) return triangles, colors

lib/matplotlib/collections.py

matplotlib

Python

4a5d09cba5f4a20e14553cebd8f70c1f34d20d35

collections.py

_convert_mesh_to_triangles

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

Deprecate draw_gouraud_triangle (#23824) * Deprecate draw_gouraud_triangle * DOC: minor rewording Co-authored-by: Elliott Sales de Andrade <[email protected]> Co-authored-by: Thomas A Caswell <[email protected]> Co-authored-by: Elliott Sales de Andrade <[email protected]>

def get_binance_available_quotes_for_each_coin() -> dict: trading_pairs = _get_trading_pairs() results = defaultdict(list) for pair in trading_pairs: results[pair["baseAsset"]].append(pair["quoteAsset"]) return results @log_start_end(log=logger)

gamestonk_terminal/cryptocurrency/due_diligence/binance_model.py

@log_start_end(log=logger)

OpenBBTerminal

Python

e1b6022b9cf156ffc0697d0d25a5ed2772ea8d68

binance_model.py

get_binance_available_quotes_for_each_coin

https://github.com/OpenBB-finance/OpenBBTerminal.git

Global plot styles (#1228) * Add default stylesheets * Add terminal style helper class and global style initialization in cfg * Style comments and docstrings * Load rich terminal theme from config file * Add application chart styles to candle charts * Add todos * Remove explicit color setting for some ta charts * Add user styles folder to gitignore * Update default stylesheets * Add matplotlib font manager support * Add matplotlib font manager support * Update docstrings and default style * Update stocks candle chart formatting (return fig to style title) * Style common ta overlap view * Make up and down market colors a part of the style helper * Update stylesheets * Style common ta volume view * Style common ta momentum view * Style common ta trend indicators view * Style common ta volatility view * Style common ta volume view * Style common ta custom indicators view * Fix styling bugs and remove the obvious time x lablel * Style charts in the covid menu * Set legend position to upper left in the mpl stylesheet * Add mpl_rcparams configs for parameters not covered by stylesheets * Remove font configuration files * Update style class utility functions * Implement passing external axes and style utility usage in ema & stoch * Add theme watermark and output helpers * Rename style to theme * Update helper usage in ta/ma and ta/stoch * Update style to theme in sample menus * Style forex (#1305) * Make tight layout optional 'cause mplfinance doesn't support it * Apply global style to the forex menu * Update code layout in oanda view and black * Style common TA (#1315) * Make tight layout optional 'cause mplfinance doesn't support it * Apply global style to the forex menu * Add linewidth to theme for use in mpf's addplots * Add vwap to the stocks notebook api * Update common/ta overlap to follow charting style * Apply style on TerminalStyle init * Enable infrastructure for excluding non-trading days from plots * Update notebook api to include there and resolve bandit warning * Update ta/common/overlap to exclude non-trading days * Enable external ax, style and non-trading days in common/ta/momentum * Enable external ax, style and non-trading days in common/ta/trend * Update vwap to the argument naming convention * Enable external ax, style and non-trading days in common/ta/volatility * Enable external ax, style and non-trading days in common/ta/volume * Enable external ax, style and non-trading days in common/ta/custom * Fix controller tests * Forgot to disable rewriting of the cassettes ... * Fix controller errors that came up because a merge conflict * Fix price label position on fib * Fix line having wrong x values in fib Co-authored-by: Colin Delahunty <[email protected]> * Style economy (#1308) * Began converting * Added alphavan_view * Added CNN View * Updated nasdaq view, fixed glitch * Added fred * Refactored URL * Theo's requested changes * Updated docstrings * Updated tests * Fixed pylint * Fixed tests * Theo changes * Econ Fix * Refactor chart style for Crypto context (#1306) * Remove mock for gff * Mock visualize_output helper function * Refactor * Fix plot helper * Update legend loc * Refactor mplfinance candle plot * Fix errors in the helper function * Fix binbook having the wrong call_ function name * Remove hardcoded style params * Resolve kwargs future warning from pandas * Remove warnings import Co-authored-by: Theodore Aptekarev <[email protected]> * funds + custom (#1311) * funds + custom * cleanup cleanup everybody everywhere * Fix external axes conditional and a typo Co-authored-by: Theodore Aptekarev <[email protected]> * Add external axes mode to covid charts (#1328) * Add portfolio menu plots (#1318) * Portfolio view plots (commenting out report stuff) * PA Menu broken. Commenting out and fix tests * portfolio optimization * comment out commented api line * Add notes on disabling the pa submenu Co-authored-by: Theodore Aptekarev <[email protected]> * Plot updates in common BA (#1335) * Add external axes support to common/ba/finbrain * Add external axes support to common/ba/twitter * Add external axes support to common/ba/google * Add external axes support to common/ba/sentimentinvestor * Add sentimentinvestor to the notebooks API * Fix tests * Etf refactor (#1323) * Refactored no ETF * Fixed gtff import * Fixed tests * Fix pie chart style * Refactored etf/candle * Added pylint fix * Fixed tests * Update candle chart layout * Update etf controller test * Remove strange binary file Co-authored-by: Theodore Aptekarev <[email protected]> * Expose ETF candle function in the notebooks API * Common BA and Common QA charts update (#1342) * Add external axes support to common/ba/finbrain * Add external axes support to common/ba/twitter * Add external axes support to common/ba/google * Add external axes support to common/ba/sentimentinvestor * Add sentimentinvestor to the notebooks API * Fix tests * Update stylesheet files * Refactor charts for common/qa * Update the forgotten line plot * Update tests * Add missing arg to a docstring * Remove scientific notation * Black imports Co-authored-by: Minh Hoang <[email protected]> * Options refactor (#1324) * Fixed alphaquery_view * finished options * Fixed pylint * Fixed tests * Fixed tests * Fixed tests * update yfinance * Tradier + Chartexchange * change mocks from gtff to theme.visualize output * tests Co-authored-by: Theodore Aptekarev <[email protected]> Co-authored-by: james <[email protected]> * Refactor Stocks menu (#1325) * Fix backtesting menu * Refactor comparison analysis * Refactor Dark pool shorts * Refactor rest of menu * Fix test * Fix tests failing * Fix tests fail * Fix test failing * Remove record mode=none to record new output * Rewrite test output * Rewrite test outputs * Adding more rewritten test output * Mock plt.show * Mock missing plt.show * Missing @pytest.mark.vcr * Updating tests : common/behavioural_analysis/finbrain * Improve notebooks API coverage for CA and DPS * Silence annoying flake8 warning Co-authored-by: Chavithra PARANA <[email protected]> Co-authored-by: Theodore Aptekarev <[email protected]> * Charts update for common/pred (#1344) * Add external axes support to common/ba/finbrain * Add external axes support to common/ba/twitter * Add external axes support to common/ba/google * Add external axes support to common/ba/sentimentinvestor * Add sentimentinvestor to the notebooks API * Fix tests * Update stylesheet files * Refactor charts for common/qa * Update the forgotten line plot * Update tests * Add missing arg to a docstring * Style pred helper and controllers * Update ETS plot * Update plots in KNN and pred helper * Update plot and pretty table for arima * Update plot for common/pred/regression * Refactor mc_view * Fix linting * Fix mypy * Move plot title to the axis level to make more vertical space Co-authored-by: Minh Hoang <[email protected]> Co-authored-by: jmaslek <[email protected]> * linter * Update common/ba test data * Change etf candle to match stock candle * try updating sia test Co-authored-by: Colin Delahunty <[email protected]> Co-authored-by: jmaslek <[email protected]> Co-authored-by: minhhoang1023 <[email protected]> Co-authored-by: Minh Hoang <[email protected]> Co-authored-by: Chavithra PARANA <[email protected]>

def get_actual_start_end_datetime_of_shift(employee, for_datetime, consider_default_shift=False): actual_shift_start = actual_shift_end = shift_details = None shift_timings_as_per_timestamp = get_employee_shift_timings( employee, for_datetime, consider_default_shift ) timestamp_list = [] for shift in shift_timings_as_per_timestamp: if shift: timestamp_list.extend([shift.actual_start, shift.actual_end]) else: timestamp_list.extend([None, None]) timestamp_index = None for index, timestamp in enumerate(timestamp_list): if timestamp and for_datetime <= timestamp: timestamp_index = index break if timestamp_index and timestamp_index % 2 == 1: shift_details = shift_timings_as_per_timestamp[int((timestamp_index - 1) / 2)] actual_shift_start = shift_details.actual_start actual_shift_end = shift_details.actual_end elif timestamp_index: shift_details = shift_timings_as_per_timestamp[int(timestamp_index / 2)] return actual_shift_start, actual_shift_end, shift_details

erpnext/hr/doctype/shift_assignment/shift_assignment.py

erpnext

Python

494bd9ef78313436f0424b918f200dab8fc7c20b

shift_assignment.py

get_actual_start_end_datetime_of_shift

https://github.com/frappe/erpnext.git

style: format code with black

def synchronize_labels(self, axis=None): if axis is None: self._deferred_index = True self._deferred_column = True elif axis == 0: self._deferred_index = True else: self._deferred_column = True

modin/core/dataframe/pandas/dataframe/dataframe.py

modin

Python

3c740dbfcdd69ddc3ab45a42be996e5c61104342

dataframe.py

synchronize_labels

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

FEAT-#3111: Ensure relabeling Modin Frame does not lose partition shape (#3662) Co-authored-by: Devin Petersohn <[email protected]> Signed-off-by: Naren Krishna <[email protected]>

def testResourceDeadlock(self):

python/ray/tune/tests/test_trial_runner_pg.py

ray

Python

976ece4bc43abdb628cf4cbffc8546abab723a6d

test_trial_runner_pg.py

testResourceDeadlock

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

[tune] Add test for heterogeneous resource request deadlocks (#21397) This adds a test for potential resource deadlocks in experiments with heterogeneous PGFs. If the PGF of a later trial becomes ready before that of a previous trial, we could run into a deadlock. This is currently avoided, but untested, flagging the code path for removal in #21387.

async def test_vocolinc_flowerbud_setup(hass): accessories = await setup_accessories_from_file(hass, "vocolinc_flowerbud.json") await setup_test_accessories(hass, accessories) await assert_devices_and_entities_created( hass, DeviceTestInfo( unique_id=HUB_TEST_ACCESSORY_ID, name="VOCOlinc-Flowerbud-0d324b", model="Flowerbud", manufacturer="VOCOlinc", sw_version="3.121.2", hw_version="0.1", serial_number="AM01121849000327", devices=[], entities=[ EntityTestInfo( entity_id="humidifier.vocolinc_flowerbud_0d324b", friendly_name="VOCOlinc-Flowerbud-0d324b", unique_id="00:00:00:00:00:00_1_30", supported_features=HumidifierEntityFeature.MODES, capabilities={ "available_modes": ["normal", "auto"], "max_humidity": 100.0, "min_humidity": 0.0, }, state="off", ), EntityTestInfo( entity_id="light.vocolinc_flowerbud_0d324b_mood_light", friendly_name="VOCOlinc-Flowerbud-0d324b Mood Light", unique_id="00:00:00:00:00:00_1_9", supported_features=0, capabilities={"supported_color_modes": ["hs"]}, state="on", ), EntityTestInfo( entity_id="number.vocolinc_flowerbud_0d324b_spray_quantity", friendly_name="VOCOlinc-Flowerbud-0d324b Spray Quantity", unique_id="00:00:00:00:00:00_1_30_38", capabilities={ "max": 5, "min": 1, "mode": NumberMode.AUTO, "step": 1, }, state="5", entity_category=EntityCategory.CONFIG, ), EntityTestInfo( entity_id="sensor.vocolinc_flowerbud_0d324b_current_humidity", friendly_name="VOCOlinc-Flowerbud-0d324b Current Humidity", unique_id="00:00:00:00:00:00_1_30_33", capabilities={"state_class": SensorStateClass.MEASUREMENT}, unit_of_measurement=PERCENTAGE, state="45.0", ), ], ), )

tests/components/homekit_controller/specific_devices/test_vocolinc_flowerbud.py

core

Python

f23b1750e85f07091eb896a0b12b8f95e5646338

test_vocolinc_flowerbud.py

test_vocolinc_flowerbud_setup

https://github.com/home-assistant/core.git

Migrate HomeKit Controller to use stable identifiers (#80064)

def closure(self, rel, depth=-1): from nltk.util import acyclic_breadth_first for synset in acyclic_breadth_first(self, rel, depth): if synset != self: yield synset from nltk.util import acyclic_depth_first as acyclic_tree from nltk.util import unweighted_minimum_spanning_tree as mst # Also add this shortcut? # from nltk.util import unweighted_minimum_spanning_digraph as umsd

nltk/corpus/reader/wordnet.py

nltk

Python

692adaff901dd9daf29400fdf3385130aefbfb2a

wordnet.py

closure

https://github.com/nltk/nltk.git

Fix some tests in Wordnet-related DocStrings

def is_on(self) -> bool: return ( self.coordinator.data[self.entity_description.key] == "TooLow" or self.coordinator.data[self.entity_description.key] == "TooHigh" )

homeassistant/components/flipr/binary_sensor.py

core

Python

c7dfd6b15a3fc9fa81d260b3dfa8a3d836f9afa8

binary_sensor.py

is_on

https://github.com/home-assistant/core.git

Add flipr battery level sensor (#81389) * Addition of battery level sensor. Correction of pylint errors * Review improvement for typing * Review improvement for typing * Correction following review

def test_archive_too_large_for_mem_cache(self, cache_set):

tests/sentry/lang/javascript/test_processor.py

sentry

Python

8cdaa4e86e8296cdbc145f2a53d3eb38cb7a1c2b

test_processor.py

test_archive_too_large_for_mem_cache

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

ref: close files explicitly in tests.sentry.lang.javascript.test_processor (#35262)

def BurstTaskRunner(): job_queue = []

src/sentry/testutils/helpers/task_runner.py

sentry

Python

ce3e457ef18fe0046d6aca0b545eac55eae8f17c

task_runner.py

BurstTaskRunner

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

feat(perf-issues): Move queue info for post_process into headers (ISP… (#40239) Re-do of https://github.com/getsentry/sentry/pull/39946 as merge conflict didn't mesh right. Sends dedicated issue category data to post_process_group call so we can route to the appropriate celery queue Will need to include changes from https://github.com/getsentry/sentry/pull/40283 to be merged first and an ensuing PR to remove the old queue.

def update_proxy_model_permissions(apps, schema_editor, reverse=False): style = color_style() Permission = apps.get_model("auth", "Permission") ContentType = apps.get_model("contenttypes", "ContentType") alias = schema_editor.connection.alias for Model in apps.get_models(): opts = Model._meta if not opts.proxy: continue proxy_default_permissions_codenames = [ "%s_%s" % (action, opts.model_name) for action in opts.default_permissions ] permissions_query = Q(codename__in=proxy_default_permissions_codenames) for codename, name in opts.permissions: permissions_query = permissions_query | Q(codename=codename, name=name) content_type_manager = ContentType.objects.db_manager(alias) concrete_content_type = content_type_manager.get_for_model( Model, for_concrete_model=True ) proxy_content_type = content_type_manager.get_for_model( Model, for_concrete_model=False ) old_content_type = proxy_content_type if reverse else concrete_content_type new_content_type = concrete_content_type if reverse else proxy_content_type try: with transaction.atomic(using=alias): Permission.objects.using(alias).filter( permissions_query, content_type=old_content_type, ).update(content_type=new_content_type) except IntegrityError: old = "{}_{}".format(old_content_type.app_label, old_content_type.model) new = "{}_{}".format(new_content_type.app_label, new_content_type.model) sys.stdout.write( style.WARNING(WARNING.format(old=old, new=new, query=permissions_query)) )

django/contrib/auth/migrations/0011_update_proxy_permissions.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

0011_update_proxy_permissions.py