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30 | 0 | 1 | 16 | tests/sentry/snuba/metrics/test_fields.py | 97,350 | feat(metrics): Adds support for CompositeEntityDerivedMetrics [INGEST-924 INGEST-1044 INGEST-1064] (#32829)
* feat(metrics): Adds support for CompositeEntityDerivedMetrics
Adds support for CompositeEntityDerivedMetrics,
Adds derived metric for sessions.errored, renames
RawMetric class to RawAggregatedMetric. Modifies
QueryBuilder to always perform post query operations
* Incorporate PR feedback | sentry | 12 | Python | 17 | test_fields.py | def test_generate_bottom_up_derived_metrics_dependencies(self):
assert list(self.sessions_errored.generate_bottom_up_derived_metrics_dependencies()) == [
(None, "session.errored_set"),
(None, "session.errored_preaggregated"),
(None, "session.errored"),
]
assert list(
MOCKED_DERIVED_METRICS[
"random_composite"
].generate_bottom_up_derived_metrics_dependencies()
) == [
(None, "session.errored_set"),
(None, "session.errored_preaggregated"),
(None, "session.errored"),
(None, "random_composite"),
]
| b52d8e5fa16670e5d4b071ca72457e187ed7eeeb | 76 | https://github.com/getsentry/sentry.git | 178 | def test_generate_bottom_up_derived_metrics_dependencies(self):
assert list(self.sessions_errored.generate_bottom_up_derived_metrics_dependencies()) == [
(None, "session.errored_set"),
(None, "session.errored_preaggregated"),
(None, "session.errored"),
]
assert list(
MOCKED_DERIVED_METRICS[
"random_composite"
].generate_bottom_up_derived_metrics_dependencies()
) == [
(None, "session.errored_set"),
(None, "session.errored_preaggregated"),
(None, "session.errore | 6 | 121 | test_generate_bottom_up_derived_metrics_dependencies |
|
35 | 0 | 1 | 9 | jaxlib/cusparse.py | 120,094 | [MHLO] Add direct MHLO lowerings for sparse primitives.
PiperOrigin-RevId: 440374054 | jax | 9 | Python | 28 | cusparse.py | def _validate_csr_mhlo(data, indices, indptr, shape):
data_type = ir.RankedTensorType(data.type)
indices_type = ir.RankedTensorType(indices.type)
indptr_type = ir.RankedTensorType(indptr.type)
nnz, = data_type.shape
assert indices_type.shape == [nnz]
assert indptr_type.element_type == indices_type.element_type
assert indptr_type.shape == [shape[0] + 1]
return data_type.element_type, indices_type.element_type, nnz
| 648a512488a5184caa8dc1bced58e9f8ab7269f2 | 86 | https://github.com/google/jax.git | 42 | def _validate_csr_mhlo(data, indices, indptr, shape):
data_type = ir.RankedTensorType(data.type)
indices_type = ir.RankedTensorType(indices.type)
indptr_type = ir.RankedTensorType(indptr.type)
nnz, = data_type.shape
assert indices_type.shape == [nnz]
assert indptr_type.element_type == indices_type.element_type
assert indptr_type.shape == [shape[0] + 1]
return da | 13 | 130 | _validate_csr_mhlo |
|
34 | 0 | 5 | 7 | src/calibre/ebooks/css_transform_rules.py | 188,915 | Automated upgrade of code to python 3.7+
Done by https://github.com/asottile/pyupgrade
Consists mainly of moving string formatting to f-strings and removing
encoding declarations | calibre | 14 | Python | 27 | css_transform_rules.py | def export_rules(serialized_rules):
lines = []
for rule in serialized_rules:
lines.extend('# ' + l for l in rule_to_text(rule).splitlines())
lines.extend('{}: {}'.format(k, v.replace('\n', ' ')) for k, v in iteritems(rule) if k in allowed_keys)
lines.append('')
return '\n'.join(lines).encode('utf-8')
| eb78a761a99ac20a6364f85e12059fec6517d890 | 84 | https://github.com/kovidgoyal/calibre.git | 63 | def export_rules(serialized_rules):
lines = | 17 | 147 | export_rules |
|
35 | 0 | 1 | 11 | python/ray/serve/tests/test_deployment_state.py | 144,680 | [serve] Introduce DeploymentStatus, poll for statuses instead of using async goals (#22121) | ray | 9 | Python | 25 | test_deployment_state.py | def test_deploy_with_consistent_constructor_failure(mock_deployment_state):
deployment_state, timer = mock_deployment_state
b_info_1, b_version_1 = deployment_info(num_replicas=2)
updating = deployment_state.deploy(b_info_1)
assert updating
assert deployment_state.curr_status_info.status == DeploymentStatus.UPDATING
_constructor_failure_loop_two_replica(deployment_state, 3)
assert deployment_state._replica_constructor_retry_counter == 6
assert deployment_state.curr_status_info.status == DeploymentStatus.FAILED
check_counts(deployment_state, total=0)
assert deployment_state.curr_status_info.message != ""
| 48adb6f7bb335b28fb0fb0d1190bd6c5dfc8ddfa | 79 | https://github.com/ray-project/ray.git | 68 | def test_deploy_with_consistent_constructor_failure(mock_deployment_state):
deployment_state, timer = mock_deployment_state
b_info_1, b_version_1 = deployment_info(num_replicas=2)
updating = deployment_state.deploy(b_info_1)
assert updating
assert deployment_state.curr_status_info.status == DeploymentStatus.UPDATING
_constructor_failure_loop_two_replica(deployment_state, 3)
assert deployment_state._replica_constructor_retry_counter == 6
assert deployment_state.curr_status_info.status == DeploymentStatus.FAILED
check_counts(deployment_state, total=0)
assert deployment_state.curr_status_info.message != ""
| 20 | 127 | test_deploy_with_consistent_constructor_failure |
|
49 | 0 | 1 | 22 | kubernetes_tests/test_kubernetes_pod_operator_backcompat.py | 42,802 | Use KubernetesHook to create api client in KubernetesPodOperator (#20578)
Add support for k8s hook in KPO; use it always (even when no conn id); continue to consider the core k8s settings that KPO already takes into account but emit deprecation warning about them.
KPO historically takes into account a few settings from core airflow cfg (e.g. verify ssl, tcp keepalive, context, config file, and in_cluster). So to use the hook to generate the client, somehow the hook has to take these settings into account. But we don't want the hook to consider these settings in general. So we read them in KPO and if necessary patch the hook and warn. | airflow | 13 | Python | 41 | test_kubernetes_pod_operator_backcompat.py | def test_envs_from_configmaps(self, mock_monitor, mock_start):
# GIVEN
configmap = 'test-configmap'
# WHEN
k = KubernetesPodOperator(
namespace='default',
image="ubuntu:16.04",
cmds=["bash", "-cx"],
arguments=["echo 10"],
labels={"foo": "bar"},
name="test",
task_id="task",
in_cluster=False,
do_xcom_push=False,
configmaps=[configmap],
)
# THEN
mock_pod = MagicMock()
mock_pod.status.phase = 'Succeeded'
mock_monitor.return_value = mock_pod
context = create_context(k)
k.execute(context)
assert mock_start.call_args[1]['pod'].spec.containers[0].env_from == [
k8s.V1EnvFromSource(config_map_ref=k8s.V1ConfigMapEnvSource(name=configmap))
]
| 60eb9e106f5915398eafd6aa339ec710c102dc09 | 135 | https://github.com/apache/airflow.git | 260 | def test_envs_from_configmaps(self, mock_monitor, mock_start):
# GIVEN
configmap = 'test-configmap'
# WHEN
k = KubernetesPodOperator(
namespace='default',
image="ubuntu:16.04",
cmds=["bash", "-cx"],
arguments=["echo 10"],
labels={"foo": "bar"},
name="test",
task_id="task",
in_cluster=False,
do_xcom_push=False,
c | 33 | 224 | test_envs_from_configmaps |
|
37 | 0 | 1 | 27 | keras/engine/data_adapter_test.py | 271,153 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | keras | 15 | Python | 28 | data_adapter_test.py | def setUp(self):
super().setUp()
self.batch_size = 5
self.numpy_input = np.zeros((50, 10))
self.numpy_target = np.ones(50)
self.tensor_input = tf.constant(2.0, shape=(50, 10))
self.tensor_target = tf.ones((50,))
self.arraylike_input = DummyArrayLike(self.numpy_input)
self.arraylike_target = DummyArrayLike(self.numpy_target)
self.dataset_input = (
tf.data.Dataset.from_tensor_slices(
(self.numpy_input, self.numpy_target)
)
.shuffle(50)
.batch(self.batch_size)
)
| 84afc5193d38057e2e2badf9c889ea87d80d8fbf | 218 | https://github.com/keras-team/keras.git | 165 | def setUp(self):
super().setUp()
self.batch_size = | 23 | 200 | setUp |
|
19 | 0 | 2 | 5 | lib/matplotlib/tri/_triangulation.py | 109,990 | Make all matplotlib.tri submodules private
Users should access all elements through the outer namespace
matplotlib.tri.
Back-compatibility for the old module names will be added in a separate
commit. If done in the same commit, git would interpret this as
a modified file plus a new file and not as a rename. With the separation
and the rename we keep the history. | matplotlib | 10 | Python | 17 | _triangulation.py | def get_trifinder(self):
if self._trifinder is None:
# Default TriFinder class.
from matplotlib.tri._trifinder import TrapezoidMapTriFinder
self._trifinder = TrapezoidMapTriFinder(self)
return self._trifinder
| cf8e04ddc1686dd285afdcc6e3ea8d9f29ff869b | 33 | https://github.com/matplotlib/matplotlib.git | 73 | def get_trifinder(self):
if self._trifinder is None:
# Default TriFinder class.
from matplotlib.tri._trifinder import TrapezoidMapTriFinder
self._trifinder = TrapezoidMapTriFinder(self)
return self._trifinder
| 6 | 55 | get_trifinder |
|
34 | 0 | 4 | 23 | homeassistant/components/ibeacon/coordinator.py | 287,742 | Handle iBeacons that broadcast multiple different uuids (#79011)
* Handle iBeacons that broadcast multiple different uuids
* fix flip-flopping between uuids
* naming | core | 13 | Python | 28 | coordinator.py | def _async_update_rssi(self) -> None:
for (
unique_id,
ibeacon_advertisement,
) in self._last_ibeacon_advertisement_by_unique_id.items():
address = unique_id.split("_")[-1]
if (
service_info := bluetooth.async_last_service_info(
self.hass, address, connectable=False
)
) and service_info.rssi != ibeacon_advertisement.rssi:
ibeacon_advertisement.update_rssi(service_info.rssi)
async_dispatcher_send(
self.hass,
signal_seen(unique_id),
ibeacon_advertisement,
)
| 02731efc4cb3f7ee94b0c08aecc10e3a5209dbf4 | 86 | https://github.com/home-assistant/core.git | 261 | def _async_update_rssi(self) -> None:
for (
unique_id,
ibeacon_advertisement,
) in self._last_ibeacon_advertisement_by_unique_id.items():
address = unique_id.split("_")[-1]
if (
servic | 17 | 134 | _async_update_rssi |
|
43 | 0 | 5 | 10 | bootloader/waflib/Tools/c_preproc.py | 263,293 | Bootloader: Building: Unpack waf's lib archive.
Doing so makes it easier to modify. This is a temporary measure until the next
waf version is released (although I'm tempted to keep it since it's much more
IDE completion friendly). | pyinstaller | 13 | Python | 33 | c_preproc.py | def eval_macro(lst, defs):
reduce_tokens(lst, defs, [])
if not lst:
raise PreprocError('missing tokens to evaluate')
if lst:
p, v = lst[0]
if p == IDENT and v not in defs:
raise PreprocError('missing macro %r' % lst)
p, v = reduce_eval(lst)
return int(v) != 0
| 64ccb7aea824fbec57f7ed1bbe483ec486183c13 | 68 | https://github.com/pyinstaller/pyinstaller.git | 89 | def eval_macro(lst, defs):
reduce_tokens(lst, defs, [])
if not lst:
raise PreprocError('missing tokens to evaluate')
if lst:
p, v = lst[0]
if p == IDENT and v not in defs:
raise PreprocError('missing macro %r' % lst)
p, v = reduce_eval(lst)
re | 10 | 110 | eval_macro |
|
27 | 0 | 1 | 20 | tests/snuba/api/endpoints/test_organization_events.py | 94,836 | fix(tests): Fix dnd backend test flakes (#37916)
This PR fixes 3 major flakes:
Fixes SENTRY-TESTS-3J5: Just sort the project id order
Fixes SENTRY-TESTS-3HQ: Flakes because we calculate the retention
in the test once and the value returned in the response is calculated
a little while after. We don't need to test for seconds granularity
so replacing seconds to 0.
Fixes SENTRY-TESTS-3J0: Successively calling before_now results in some flakes
particularly in tests that are calculating aggregates
on transaction.duration. Introduced a load_data method
that takes a datetime object timestamp and a timedelta duration
calculates the offset based on timestamp to get start_timestamp. | sentry | 14 | Python | 18 | test_organization_events.py | def test_in_query_events_stack(self):
test_js = self.store_event(
self.load_data(
platform="javascript",
timestamp=before_now(minutes=10),
duration=timedelta(seconds=5),
),
project_id=self.project.id,
)
test_java = self.store_event(
self.load_data(
platform="java",
timestamp=before_now(minutes=10),
duration=timedelta(seconds=5),
),
project_id=self.project.id,
)
self.run_test_in_query(
"stack.filename:[../../sentry/scripts/views.js]", [test_js], [test_java]
)
| ab993b32614bb83d17d10e1041817e43dd6f5980 | 105 | https://github.com/getsentry/sentry.git | 235 | def test_in_query_events_stack(self):
test_js = self.store_event(
self.load_data(
platform="javascript",
timestamp=before_now(minutes=10),
duration=timedelta(seconds=5),
),
project_id=self.project.id,
)
test_java = self.store_event(
self.load_data(
platform="java",
timestamp=before_now(minutes=10),
duration=timedelt | 17 | 161 | test_in_query_events_stack |
|
49 | 0 | 1 | 13 | pandas/tests/arrays/test_datetimes.py | 169,899 | REF: _reso->_creso (#49107) | pandas | 11 | Python | 25 | test_datetimes.py | def test_add_timedeltalike_scalar_mismatched_reso(self, dta_dti, scalar):
dta, dti = dta_dti
td = pd.Timedelta(scalar)
exp_reso = max(dta._creso, td._creso)
exp_unit = npy_unit_to_abbrev(exp_reso)
expected = (dti + td)._data._as_unit(exp_unit)
result = dta + scalar
tm.assert_extension_array_equal(result, expected)
result = scalar + dta
tm.assert_extension_array_equal(result, expected)
expected = (dti - td)._data._as_unit(exp_unit)
result = dta - scalar
tm.assert_extension_array_equal(result, expected)
| 90b4add77859d1349530fff3c8cadeef95f36f39 | 107 | https://github.com/pandas-dev/pandas.git | 132 | def test_add_timedeltalike_scalar_mismatched_reso(self, dta_dti, scalar):
dta, dti = dta_dti
td = pd.Timedelta(scalar)
exp_reso = max(dta._creso, td._creso)
exp_unit = npy_un | 20 | 166 | test_add_timedeltalike_scalar_mismatched_reso |
|
211 | 0 | 12 | 42 | PPOCRLabel/PPOCRLabel.py | 23,691 | new | PaddleOCR | 20 | Python | 126 | PPOCRLabel.py | def cellreRecognition(self):
img = cv2.imread(self.filePath)
for shape in self.canvas.selectedShapes:
box = [[int(p.x()), int(p.y())] for p in shape.points]
if len(box) > 4:
box = self.gen_quad_from_poly(np.array(box))
assert len(box) == 4
# pad around bbox for better text recognition accuracy
_box = boxPad(box, img.shape, 6)
img_crop = get_rotate_crop_image(img, np.array(_box, np.float32))
if img_crop is None:
msg = 'Can not recognise the detection box in ' + self.filePath + '. Please change manually'
QMessageBox.information(self, "Information", msg)
return
# merge the text result in the cell
texts = ''
probs = 0. # the probability of the cell is avgerage prob of every text box in the cell
bboxes = self.ocr.ocr(img_crop, det=True, rec=False, cls=False)
if len(bboxes) > 0:
bboxes.reverse() # top row text at first
for _bbox in bboxes:
patch = get_rotate_crop_image(img_crop, np.array(_bbox, np.float32))
rec_res = self.ocr.ocr(patch, det=False, rec=True, cls=False)
text = rec_res[0][0]
if text != '':
texts += text + (' ' if text[0].isalpha() else '') # add space between english word
probs += rec_res[0][1]
probs = probs / len(bboxes)
result = [(texts.strip(), probs)]
if result[0][0] != '':
result.insert(0, box)
print('result in reRec is ', result)
if result[1][0] == shape.label:
print('label no change')
else:
shape.label = result[1][0]
else:
print('Can not recognise the box')
if self.noLabelText == shape.label:
print('label no change')
else:
shape.label = self.noLabelText
self.singleLabel(shape)
self.setDirty()
| 8b228a1f9b011aba935963431cadb81c7fe361d5 | 378 | https://github.com/PaddlePaddle/PaddleOCR.git | 827 | def cellreRecognition(self):
img = cv2.imread(self.filePath)
for shape in self.canvas.selectedShapes:
box = [[int(p.x()), int(p.y())] for p in shape.points]
if len(box) > 4:
box = self.gen_quad_from_poly(np.array(box))
assert len(box) == 4
# pad around bbox for better text recognition accuracy
_box = boxPad(box, img.shape, 6)
img_crop = get_rotate_crop_image(img, np.array(_box, np.float32))
if img_crop is None:
msg = 'Can not recognise the detection box in ' + self.filePath + '. Please change manually'
QMessageBox.information(self, "Information", msg)
return
# merge the text result in the cell
texts = ''
probs = 0. # the probability of the cell is avgerage prob of every text box in the cell
bboxes = self.ocr.ocr(img_crop, det=True, rec=False, cls=False)
if len(bboxes) > 0:
bboxes.reverse() # top row text at first
for _bbox in bboxes:
patch = get_rotate_crop_image(img_crop, np.array(_bbox, np.float32))
rec_res = self.ocr.ocr(patch, det=False, rec=True, cls=False)
text = rec_res[0][0]
if text != '':
texts += text + (' ' if text[0].isalpha() else '') # add space between english word
probs += rec_res[0][1]
probs = probs / len(bboxes)
result = [(texts.strip(), probs)]
if result[0][0] != '':
result.insert(0, box)
print('resu | 48 | 610 | cellreRecognition |
|
36 | 1 | 2 | 15 | test/units/galaxy/test_collection.py | 266,413 | ansible-galaxy - fix the --ignore-certs flag for the implicit galaxy server (#76735)
* ansible-galaxy - fix the --ignore-certs flag for the implicit galaxy server
* changelog
* Add a test without the server config
* Fix respecting --ignore-certs for individual --server URLs also
* Update changelogs/fragments/76735-ansible-galaxy-fix-ignore-certs.yaml | ansible | 10 | Python | 33 | test_collection.py | def test_validate_certs(global_ignore_certs, monkeypatch):
cli_args = [
'ansible-galaxy',
'collection',
'install',
'namespace.collection:1.0.0',
]
if global_ignore_certs:
cli_args.append('--ignore-certs')
galaxy_cli = GalaxyCLI(args=cli_args)
mock_execute_install = MagicMock()
monkeypatch.setattr(galaxy_cli, '_execute_install_collection', mock_execute_install)
galaxy_cli.run()
assert len(galaxy_cli.api_servers) == 1
assert galaxy_cli.api_servers[0].validate_certs is not global_ignore_certs
@pytest.mark.parametrize('global_ignore_certs', [True, False]) | 76220c4a7bf90c97113fe104ea33957a9881b8a9 | @pytest.mark.parametrize('global_ignore_certs', [True, False]) | 77 | https://github.com/ansible/ansible.git | 96 | def test_validate_certs(global_ignore_certs, monkeypatch):
cli_args = [
'ansible-galaxy',
'collection',
'install',
'namespace.collection:1.0.0',
]
if global_ignore_certs:
cli_args.append('--ignore-certs')
galaxy_cli = Galaxy | 18 | 153 | test_validate_certs |
254 | 0 | 14 | 47 | .venv/lib/python3.8/site-packages/pip/_internal/cli/parser.py | 60,539 | upd; format | transferlearning | 18 | Python | 148 | parser.py | def _update_defaults(self, defaults):
# type: (Dict[str, Any]) -> Dict[str, Any]
# Accumulate complex default state.
self.values = optparse.Values(self.defaults)
late_eval = set()
# Then set the options with those values
for key, val in self._get_ordered_configuration_items():
# '--' because configuration supports only long names
option = self.get_option("--" + key)
# Ignore options not present in this parser. E.g. non-globals put
# in [global] by users that want them to apply to all applicable
# commands.
if option is None:
continue
assert option.dest is not None
if option.action in ("store_true", "store_false"):
try:
val = strtobool(val)
except ValueError:
self.error(
"{} is not a valid value for {} option, " # noqa
"please specify a boolean value like yes/no, "
"true/false or 1/0 instead.".format(val, key)
)
elif option.action == "count":
with suppress(ValueError):
val = strtobool(val)
with suppress(ValueError):
val = int(val)
if not isinstance(val, int) or val < 0:
self.error(
"{} is not a valid value for {} option, " # noqa
"please instead specify either a non-negative integer "
"or a boolean value like yes/no or false/true "
"which is equivalent to 1/0.".format(val, key)
)
elif option.action == "append":
val = val.split()
val = [self.check_default(option, key, v) for v in val]
elif option.action == "callback":
assert option.callback is not None
late_eval.add(option.dest)
opt_str = option.get_opt_string()
val = option.convert_value(opt_str, val)
# From take_action
args = option.callback_args or ()
kwargs = option.callback_kwargs or {}
option.callback(option, opt_str, val, self, *args, **kwargs)
else:
val = self.check_default(option, key, val)
defaults[option.dest] = val
for key in late_eval:
defaults[key] = getattr(self.values, key)
self.values = None
return defaults
| f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | 308 | https://github.com/jindongwang/transferlearning.git | 1,029 | def _update_defaults(self, defaults):
# type: (Dict[str, Any]) -> Dict[str, Any]
# Accumulate complex default state.
self.values = optparse.Values(self.defaults)
late_eval = set()
| 35 | 518 | _update_defaults |
|
24 | 0 | 2 | 8 | ppdet/modeling/backbones/mobileone.py | 210,982 | Add SIoU and MobileOne block (#6312)
* Add SIoU and MobileOne block
* add paddle copyright
* mobileone block k>1 bugfix
* format code style | PaddleDetection | 13 | Python | 22 | mobileone.py | def _pad_1x1_to_3x3_tensor(self, kernel1x1):
if kernel1x1 is None:
return 0
else:
padding_size = (self.kernel_size - 1) // 2
return nn.functional.pad(
kernel1x1,
[padding_size, padding_size, padding_size, padding_size])
| 6d91289fc71f4b7440515c7eed4302066a524a22 | 45 | https://github.com/PaddlePaddle/PaddleDetection.git | 100 | def _pad_1x1_to_3x3_tensor(self, kernel1x1):
if kernel1x1 is None:
return 0
else:
padding_size = (self.kernel_size - 1) // 2
return nn.functional.pad(
kernel1x1,
| 8 | 68 | _pad_1x1_to_3x3_tensor |
|
492 | 0 | 19 | 112 | sklearn/ensemble/_hist_gradient_boosting/grower.py | 261,256 | ENH FEA add interaction constraints to HGBT (#21020)
Co-authored-by: Loïc Estève <[email protected]> | scikit-learn | 16 | Python | 251 | grower.py | def split_next(self):
# Consider the node with the highest loss reduction (a.k.a. gain)
node = heappop(self.splittable_nodes)
tic = time()
(
sample_indices_left,
sample_indices_right,
right_child_pos,
) = self.splitter.split_indices(node.split_info, node.sample_indices)
self.total_apply_split_time += time() - tic
depth = node.depth + 1
n_leaf_nodes = len(self.finalized_leaves) + len(self.splittable_nodes)
n_leaf_nodes += 2
left_child_node = TreeNode(
depth,
sample_indices_left,
node.split_info.sum_gradient_left,
node.split_info.sum_hessian_left,
value=node.split_info.value_left,
)
right_child_node = TreeNode(
depth,
sample_indices_right,
node.split_info.sum_gradient_right,
node.split_info.sum_hessian_right,
value=node.split_info.value_right,
)
node.right_child = right_child_node
node.left_child = left_child_node
# set start and stop indices
left_child_node.partition_start = node.partition_start
left_child_node.partition_stop = node.partition_start + right_child_pos
right_child_node.partition_start = left_child_node.partition_stop
right_child_node.partition_stop = node.partition_stop
# set interaction constraints (the indices of the constraints sets)
if self.interaction_cst is not None:
# Calculate allowed_features and interaction_cst_indices only once. Child
# nodes inherit them before they get split.
(
left_child_node.allowed_features,
left_child_node.interaction_cst_indices,
) = self._compute_interactions(node)
right_child_node.interaction_cst_indices = (
left_child_node.interaction_cst_indices
)
right_child_node.allowed_features = left_child_node.allowed_features
if not self.has_missing_values[node.split_info.feature_idx]:
# If no missing values are encountered at fit time, then samples
# with missing values during predict() will go to whichever child
# has the most samples.
node.split_info.missing_go_to_left = (
left_child_node.n_samples > right_child_node.n_samples
)
self.n_nodes += 2
self.n_categorical_splits += node.split_info.is_categorical
if self.max_leaf_nodes is not None and n_leaf_nodes == self.max_leaf_nodes:
self._finalize_leaf(left_child_node)
self._finalize_leaf(right_child_node)
self._finalize_splittable_nodes()
return left_child_node, right_child_node
if self.max_depth is not None and depth == self.max_depth:
self._finalize_leaf(left_child_node)
self._finalize_leaf(right_child_node)
return left_child_node, right_child_node
if left_child_node.n_samples < self.min_samples_leaf * 2:
self._finalize_leaf(left_child_node)
if right_child_node.n_samples < self.min_samples_leaf * 2:
self._finalize_leaf(right_child_node)
if self.with_monotonic_cst:
# Set value bounds for respecting monotonic constraints
# See test_nodes_values() for details
if (
self.monotonic_cst[node.split_info.feature_idx]
== MonotonicConstraint.NO_CST
):
lower_left = lower_right = node.children_lower_bound
upper_left = upper_right = node.children_upper_bound
else:
mid = (left_child_node.value + right_child_node.value) / 2
if (
self.monotonic_cst[node.split_info.feature_idx]
== MonotonicConstraint.POS
):
lower_left, upper_left = node.children_lower_bound, mid
lower_right, upper_right = mid, node.children_upper_bound
else: # NEG
lower_left, upper_left = mid, node.children_upper_bound
lower_right, upper_right = node.children_lower_bound, mid
left_child_node.set_children_bounds(lower_left, upper_left)
right_child_node.set_children_bounds(lower_right, upper_right)
# Compute histograms of children, and compute their best possible split
# (if needed)
should_split_left = not left_child_node.is_leaf
should_split_right = not right_child_node.is_leaf
if should_split_left or should_split_right:
# We will compute the histograms of both nodes even if one of them
# is a leaf, since computing the second histogram is very cheap
# (using histogram subtraction).
n_samples_left = left_child_node.sample_indices.shape[0]
n_samples_right = right_child_node.sample_indices.shape[0]
if n_samples_left < n_samples_right:
smallest_child = left_child_node
largest_child = right_child_node
else:
smallest_child = right_child_node
largest_child = left_child_node
# We use the brute O(n_samples) method on the child that has the
# smallest number of samples, and the subtraction trick O(n_bins)
# on the other one.
tic = time()
smallest_child.histograms = self.histogram_builder.compute_histograms_brute(
smallest_child.sample_indices
)
largest_child.histograms = (
self.histogram_builder.compute_histograms_subtraction(
node.histograms, smallest_child.histograms
)
)
self.total_compute_hist_time += time() - tic
tic = time()
if should_split_left:
self._compute_best_split_and_push(left_child_node)
if should_split_right:
self._compute_best_split_and_push(right_child_node)
self.total_find_split_time += time() - tic
# Release memory used by histograms as they are no longer needed
# for leaf nodes since they won't be split.
for child in (left_child_node, right_child_node):
if child.is_leaf:
del child.histograms
# Release memory used by histograms as they are no longer needed for
# internal nodes once children histograms have been computed.
del node.histograms
return left_child_node, right_child_node
| 5ceb8a6a031ddff26a7ede413db1b53edb64166a | 642 | https://github.com/scikit-learn/scikit-learn.git | 1,959 | def split_next(self):
# Consider the node with the highest loss reduction (a.k.a. gain)
node = heappop(self.splittable_nodes)
tic = time()
(
sample_indices_left,
sample_indices_right,
right_child_pos,
) = self.splitter.split_indices(node.split_info, node.sample_indices)
self.total_apply_split_time += time() - tic
depth = node.depth + 1
n_leaf_nodes = len(self.finalized_leaves) + len(self.splittable_nodes)
n_leaf_nodes += 2
left_child_node = TreeNode(
depth,
sample_indices_left,
node.split_info.sum_gradient_left,
node.split_info.sum_hessian_left,
value=node.split_info.value_left,
)
right_child_node = TreeNode(
depth,
sample_indices_right,
node.split_info.sum_gradient_right,
node.split_info.sum_hessian_right,
value=node.split_info.value_right,
)
node.right_child = right_child_node
node.left_child = left_child_node
# set start and stop indices
left_child_node.partition_start = node.partition_start
left_child_node.partition_stop = node.partition_start + right_child_pos
right_child_node.partition_start = left_child_node.partition_stop
right_child_node.partition_stop = node.partition_stop
# set interaction constraints (the indices of the constraints sets)
if self.interaction_cst is not None:
# Calculate allowed_features and interaction_cst_indices only once. Child
# nodes inherit them before they get split.
(
left_child_node.allowed_features,
left_child_node.interaction_cst_indices,
) = self._compute_interactions(node)
right_child_node.interaction_cst_indices = (
left_child_node.interaction_cst_indices
)
right_child_node.allowed_features = left_child_node.allowed_features
if not self.has_missing_values[node.split_info.feature_idx]:
# If no missing values are encountered at fit time, then samples
# with missing values during predict() will go to whichever child
# has the most samples.
node.split_info.missing_go_to_left = (
left_child_node.n_samples > right_child_node.n_samples
)
self.n_nodes += 2
self.n_categorical_splits += node.split_info.is_categorical
if self.max_leaf_nodes is not None and n_leaf_nodes == self.max_leaf_nodes:
self._finalize_leaf(left_child_node)
self._finalize_leaf(right_child_node)
self._finalize_splittable_nodes()
return left_child_node, right_child_node
if self.max_depth is not None and depth == self.max_depth:
self._finalize_leaf(left_child_node)
self._finalize_leaf(right_child_node)
return left_child_node, right_child_node
if left_child_node.n_samples < self.min_samples_leaf * 2:
self._finalize_leaf(left_child_node)
if right_child_node.n_samples < self.min_samples_leaf * 2:
self._finalize_leaf(right_child_node)
if self.with_monotonic_cst:
# Set value bounds for respecting monotonic constraints
# See test_nodes_values() for details
if (
self.monotonic_cst[node.split_info.feature_idx]
== MonotonicConstraint.NO_CST
):
lower_left = lower_right = node.children_lower_bound
upper_left = upper_right = node.children_upper_bound
else:
mid = (left_child_node.value + right_child_node.value) / 2
if (
self.monotonic_cst[node.split_info.feature_idx]
== MonotonicConstraint.POS
):
lower_left, upper_left = node.children_lower_bound, mid
lower_right, upper_right = mid, node.children_upper_bound
else: # NEG
lower_left, upper_left = mid, node.children_upper_bound
lower_right, upper_right = node.children_lower_bound, mid
left_child_node.set_children_bounds(lower_left, upp | 78 | 1,022 | split_next |
|
6 | 0 | 1 | 2 | tests/test_relations.py | 48,688 | Fix Pytest's deprecation warnings about nose usage (#8758)
Pytest 7.2.0 deprecated plain `setup` and `teardown` functions and
methods as nose idioms:
https://docs.pytest.org/en/latest/changelog.html#pytest-7-2-0-2022-10-23
`setup` can be safely replaced with `setup_method`:
https://docs.pytest.org/en/stable/deprecations.html#setup-teardown
Fixes: https://github.com/encode/django-rest-framework/issues/8757
Signed-off-by: Stanislav Levin <[email protected]>
Signed-off-by: Stanislav Levin <[email protected]> | django-rest-framework | 9 | Python | 6 | test_relations.py | def setup_method(self):
self.default_hyperlink = serializers.Hyperlink('http://example.com', 'test')
| 78cdae69997c9fd95211ec15fb4e21f4cd45e30a | 17 | https://github.com/encode/django-rest-framework.git | 12 | def setup_method(self):
self.defaul | 5 | 31 | setup_method |
|
17 | 0 | 1 | 7 | homeassistant/components/demo/mailbox.py | 307,676 | Add demo to strict-typing (#77596)
* Add demo to strict-typing
* Adjust component
* Adjust PR
* Update homeassistant/components/demo/mailbox.py
Co-authored-by: Marc Mueller <[email protected]> | core | 12 | Python | 17 | mailbox.py | async def async_get_messages(self) -> list[dict[str, Any]]:
return sorted(
self._messages.values(),
key=lambda item: item["info"]["origtime"], # type: ignore[no-any-return]
reverse=True,
)
| efb482fb1dcf29468e50fca98f046d551d6355c7 | 45 | https://github.com/home-assistant/core.git | 72 | async def async_get_messages(self) -> list[dict[str, Any]]:
| 12 | 74 | async_get_messages |
|
20 | 0 | 1 | 12 | rllib/offline/estimators/tests/test_dr_learning.py | 126,931 | [RLlib] Fix test_ope flakiness (#27676) | ray | 9 | Python | 19 | test_dr_learning.py | def test_dr_expert_policy_mixed_data(self):
print("Test DoublyRobust on expert policy on mixed dataset")
check_estimate(
estimator_cls=DoublyRobust,
gamma=self.gamma,
q_model_config=self.q_model_config,
policy=self.expert_policy,
batch=self.mixed_batch,
mean_ret=self.expert_reward,
std_ret=self.expert_std,
seed=SEED,
)
| 4607e788c1277f9396d7f45ea112b2d551383499 | 56 | https://github.com/ray-project/ray.git | 128 | def test_dr_expert_policy_mixed_data(self):
print("Test DoublyRobust on expert policy on mixed dataset")
check_estimate(
estimator_cls=DoublyRobust,
gamma=self.gamma,
q_model_config=self.q_mo | 18 | 82 | test_dr_expert_policy_mixed_data |
|
29 | 1 | 4 | 9 | erpnext/e_commerce/shopping_cart/cart.py | 65,808 | style: format code with black | erpnext | 10 | Python | 26 | cart.py | def get_shipping_addresses(party=None):
if not party:
party = get_party()
addresses = get_address_docs(party=party)
return [
{"name": address.name, "title": address.address_title, "display": address.display}
for address in addresses
if address.address_type == "Shipping"
]
@frappe.whitelist() | 494bd9ef78313436f0424b918f200dab8fc7c20b | @frappe.whitelist() | 56 | https://github.com/frappe/erpnext.git | 19 | def get_shipping_addresses(party=None):
if not party:
party = get_party()
addresses = get_address_docs(party=party)
| 12 | 105 | get_shipping_addresses |
85 | 0 | 1 | 13 | pandas/tests/tools/test_to_datetime.py | 172,096 | PDEP0004: implementation (#49024)
* :wastebasket: deprecate infer_datetime_format, make strict
* :rotating_light: add warning about dayfirst
* :white_check_mark: add/update tests
* :rotating_light: add warning if format cant be guessed
* :goal_net: catch warnings
* :memo: update docs
* :memo: add example of reading csv file with mixed formats
* :wastebasket: removed now outdated tests / clean inputs
* :memo: clarify whatsnew and user-guide
* :art:
* guess %Y-%m format
* Detect format from first non-na, but also exclude now and today
* :white_check_mark: fixup tests based on now and today parsing
* fixup after merge
* fixup after merge
* fixup test
* remove outdated doctest
* xfail test based on issue 49767
* wip
* add back examples of formats which can be guessed
* start fixing up
* fixups from reviews
* lint
* put tests back
* shorten diff
* add example of string which cannot be guessed
* add deprecated directive, construct expected explicitly, explicit UserWarning, reword row-wise and column-wise
* remove redundant example
* restore newline
* double backticks around False, explicitly raise UserWarning
* reword warning
* test both dayfirst True and False
* postmerge fixup
* unimportant typo to restart CI
Co-authored-by: MarcoGorelli <> | pandas | 11 | Python | 57 | test_to_datetime.py | def test_parsers_timestring(self, date_str, exp_def):
# must be the same as dateutil result
exp_now = parse(date_str)
result1, _ = parsing.parse_time_string(date_str)
with tm.assert_produces_warning(UserWarning, match="Could not infer format"):
result2 = to_datetime(date_str)
result3 = to_datetime([date_str])
result4 = Timestamp(date_str)
result5 = DatetimeIndex([date_str])[0]
# parse time string return time string based on default date
# others are not, and can't be changed because it is used in
# time series plot
assert result1 == exp_def
assert result2 == exp_now
assert result3 == exp_now
assert result4 == exp_now
assert result5 == exp_now
| 1d5f05c33c613508727ee7b971ad56723d474446 | 88 | https://github.com/pandas-dev/pandas.git | 204 | def test_parsers_timestring(self, date_str, exp_def):
# must be the same as dateutil result
exp_now = parse(date_str)
result1, _ = parsing.parse_time_string(date_str)
with tm.assert_produces_warning(UserWarning, match="Could not infer format"):
result2 = to_datetime(date_str)
result3 = to_datetime([date_str])
result4 = Timestamp(date_str)
result5 = DatetimeIndex([date_str])[0]
# parse time string return time string based on default date
# others are not, and can't be changed because it is used in
# time series plot
assert result1 == exp_def
assert result2 == exp_now
| 21 | 145 | test_parsers_timestring |
|
29 | 0 | 1 | 15 | keras/dtensor/layers_test.py | 270,583 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | keras | 13 | Python | 26 | layers_test.py | def setUp(self):
super().setUp()
backend.enable_tf_random_generator()
tf_utils.set_random_seed(1337)
global_ids = test_util.create_device_ids_array((2, 2))
local_device_ids = np.ravel(global_ids).tolist()
mesh_dict = {
"CPU": dtensor.Mesh(
["X", "Y"],
global_ids,
local_device_ids,
test_util.create_device_list((2, 2), "CPU"),
)
}
self.mesh = self.configTestMesh(mesh_dict)
| 84afc5193d38057e2e2badf9c889ea87d80d8fbf | 91 | https://github.com/keras-team/keras.git | 166 | def setUp(self):
super(). | 20 | 149 | setUp |
|
75 | 0 | 2 | 12 | src/pip/_internal/models/installation_report.py | 174,577 | install report: add version field
Also, affirm the experimental status of the feature. | pip | 13 | Python | 64 | installation_report.py | def to_dict(self) -> Dict[str, Any]:
return {
"version": "0",
"pip_version": __version__,
"install": {
canonicalize_name(ireq.metadata["Name"]): self._install_req_to_dict(
ireq
)
for ireq in self._install_requirements
},
# https://peps.python.org/pep-0508/#environment-markers
# TODO: currently, the resolver uses the default environment to evaluate
# environment markers, so that is what we report here. In the future, it
# should also take into account options such as --python-version or
# --platform, perhaps under the form of an environment_override field?
# https://github.com/pypa/pip/issues/11198
"environment": default_environment(),
}
| 1fbfdc44233486299db4d4364cf8cc8ef98ceacb | 56 | https://github.com/pypa/pip.git | 273 | def to_dict(self) -> Dict[str, Any]:
| 12 | 99 | to_dict |
|
84 | 0 | 8 | 22 | src/sentry/api/fields/actor.py | 88,065 | ref(hybrid-cloud): Add user services. Start tagging some model tests as stable (#40614)
Notifications uses new hybrid cloud APIUser
Co-authored-by: Mike Ihbe <[email protected]>
Co-authored-by: Zachary Collins <[email protected]>
Co-authored-by: Zach Collins <[email protected]> | sentry | 16 | Python | 59 | actor.py | def to_internal_value(self, data):
if not data:
return None
try:
actor = ActorTuple.from_actor_identifier(data)
except Exception:
raise serializers.ValidationError(
"Could not parse actor. Format should be `type:id` where type is `team` or `user`."
)
try:
obj: APIUser | Team = actor.resolve()
except (Team.DoesNotExist, User.DoesNotExist):
raise serializers.ValidationError(f"{actor.type.__name__} does not exist")
if actor.type == Team:
if obj.organization != self.context["organization"]:
raise serializers.ValidationError("Team is not a member of this organization")
elif actor.type == User:
if not OrganizationMember.objects.filter(
organization=self.context["organization"], user_id=obj.id
).exists():
raise serializers.ValidationError("User is not a member of this organization")
return actor
| b38f59d9f6d9eedd7ce0606805df7c072addb000 | 135 | https://github.com/getsentry/sentry.git | 298 | def to_internal_value(self, data):
if not data:
return None
try:
actor = Act | 25 | 233 | to_internal_value |
|
32 | 0 | 3 | 31 | erpnext/patches/v8_7/sync_india_custom_fields.py | 68,999 | fix: remove HR/Payroll patches | erpnext | 12 | Python | 28 | sync_india_custom_fields.py | def execute():
company = frappe.get_all("Company", filters={"country": "India"})
if not company:
return
frappe.reload_doc("accounts", "doctype", "tax_category")
for doctype in ["Sales Invoice", "Delivery Note", "Purchase Invoice"]:
frappe.db.sql(
,
doctype,
)
make_custom_fields()
frappe.db.sql(
)
frappe.db.sql(
)
| 930e557fc6e6bdd515984e2f66ab5cea29101bae | 126 | https://github.com/frappe/erpnext.git | 17 | def execute():
company = frappe.get_all("Company", filters={"country": "India"})
if not company:
| 10 | 141 | execute |
|
104 | 0 | 1 | 25 | tests/rest/client/test_sync.py | 248,164 | Implement changes to MSC2285 (hidden read receipts) (#12168)
* Changes hidden read receipts to be a separate receipt type
(instead of a field on `m.read`).
* Updates the `/receipts` endpoint to accept `m.fully_read`. | synapse | 11 | Python | 69 | test_sync.py | def test_knock_room_state(self) -> None:
# Knock on a room
channel = self.make_request(
"POST",
f"/_matrix/client/r0/knock/{self.room_id}",
b"{}",
self.knocker_tok,
)
self.assertEqual(200, channel.code, channel.result)
# We expect to see the knock event in the stripped room state later
self.expected_room_state[EventTypes.Member] = {
"content": {"membership": "knock", "displayname": "knocker"},
"state_key": "@knocker:test",
}
# Check that /sync includes stripped state from the room
channel = self.make_request(
"GET",
self.url % self.next_batch,
access_token=self.knocker_tok,
)
self.assertEqual(channel.code, 200, channel.json_body)
# Extract the stripped room state events from /sync
knock_entry = channel.json_body["rooms"]["knock"]
room_state_events = knock_entry[self.room_id]["knock_state"]["events"]
# Validate that the knock membership event came last
self.assertEqual(room_state_events[-1]["type"], EventTypes.Member)
# Validate the stripped room state events
self.check_knock_room_state_against_room_state(
room_state_events, self.expected_room_state
)
| 116a4c8340b729ffde43be33df24d417384cb28b | 157 | https://github.com/matrix-org/synapse.git | 354 | def test_knock_room_state(self) -> None:
# Knock on a room
channel = self.make_request(
"POST",
f"/_matrix/client/r0/knock/{self.room_id}",
b"{}",
self.knocker_tok,
)
self.assertEqual(200, channel.code, channel.result)
# We expect to see the knock event in the stripped room state later
self.expected_room_state[EventTypes.Member] = {
"content": {"membership": "knock", "displayname": "knocker"},
"state_key": "@knocker:te | 19 | 271 | test_knock_room_state |
|
41 | 0 | 1 | 15 | tests/sentry/snuba/metrics/test_query.py | 85,939 | feat(metrics): Make metrics layer accept MRI directly [TET-321] (#39003)
The metrics layer entrypoint which is the `MetricsQuery` object used to
accept public names. As public names is not the naming contract we
guarantee not to change, this PR allows `MetricQuery` object to directly
accept MRI as that is the naming contract we guarantee | sentry | 16 | Python | 36 | test_query.py | def test_validate_distribution_functions_in_orderby():
# Validate no exception is raised when all orderBy fields are presented the select
metric_field_1 = MetricField(op="avg", metric_mri=TransactionMRI.DURATION.value)
metric_field_2 = MetricField(op="p50", metric_mri=TransactionMRI.DURATION.value)
metrics_query_dict = (
MetricsQueryBuilder()
.with_select([metric_field_1, metric_field_2])
.with_orderby(
[
OrderBy(field=metric_field_1, direction=Direction.ASC),
OrderBy(field=metric_field_2, direction=Direction.ASC),
]
)
.to_metrics_query_dict()
)
MetricsQuery(**metrics_query_dict)
| 04077133ca6e56647aca948e5ac21d3260b81f3f | 93 | https://github.com/getsentry/sentry.git | 145 | def test_validate_distribution_functions_in_orderby():
# Validate no exception is raised when all orderBy fields are presented the select
metric_field_1 = MetricField(op="avg", metric_mri=TransactionMRI.DURATION.value)
metric_field_2 = MetricField(op="p50", metric_mri=TransactionMRI.DURATION.value)
metrics_query_dict = (
MetricsQueryBuilder()
.with_select([metri | 20 | 148 | test_validate_distribution_functions_in_orderby |
|
102 | 0 | 3 | 18 | lib/ansible/modules/git.py | 266,557 | Bypass fragile git ssh wrapper (#73404)
git module now uses env vars exclusively
- updated docs to clarify usage
- now env vars append instead of overwrite to allow existing custom setups to keep working
fixes #38104, #64673, #64674
- added note for hostkeychecking more securely
fixes #69846
- keep script cause old versions still choke on env
- env var cannot hold more than 'command' for older versions
- all ssh_opts in one place | ansible | 15 | Python | 83 | git.py | def write_ssh_wrapper(module):
try:
# make sure we have full permission to the module_dir, which
# may not be the case if we're sudo'ing to a non-root user
if os.access(module.tmpdir, os.W_OK | os.R_OK | os.X_OK):
fd, wrapper_path = tempfile.mkstemp(prefix=module.tmpdir + '/')
else:
raise OSError
except (IOError, OSError):
fd, wrapper_path = tempfile.mkstemp()
# use existing git_ssh/ssh_command, fallback to 'ssh'
template = b( % os.environ.get('GIT_SSH', os.environ.get('GIT_SSH_COMMAND', 'ssh')))
# write it
with os.fdopen(fd, 'w+b') as fh:
fh.write(template)
# set execute
st = os.stat(wrapper_path)
os.chmod(wrapper_path, st.st_mode | stat.S_IEXEC)
module.debug('Wrote temp git ssh wrapper (%s): %s' % (wrapper_path, template))
# ensure we cleanup after ourselves
module.add_cleanup_file(path=wrapper_path)
return wrapper_path
| b493c590bcee9b64e8ae02c17d4fde2331e0598b | 154 | https://github.com/ansible/ansible.git | 208 | def write_ssh_wrapper(module):
try:
# make sure we have full permission to the module_dir, which
# may not be the case if we're sudo'ing to a non-root user
if os.access(module.tmpdir, os.W_OK | os.R_OK | os.X_OK):
fd, wrapper_path = tempfile.mkstemp(prefix=module.tmpdir + '/')
else:
raise OSError
except (IOError, OSError):
fd, wrapper_path = tempfile.mkstemp()
# use existing git_ssh/ssh_command, fallback to 'ssh'
template = b( % os.environ.get('GIT_SSH', os.environ.get('GIT_SSH_COMMAND', 'ss | 30 | 265 | write_ssh_wrapper |
|
120 | 0 | 1 | 15 | sympy/integrals/tests/test_integrals.py | 198,654 | fix(integrals): fix degeneracy checking in heurisch
Previously heurisch used solve with a single equation rather than a list
containing that equation i.e. solve(eq) rather than solve([eq]). This
takes different codepaths in solve and the [eq] codepath is more robust.
This commit changes heurisch to use [eq] and also changes the Piecewise
handling routine to produce deterministic output when there are multiple
degenerate cases to handle. | sympy | 19 | Python | 82 | test_integrals.py | def test_issue_23718():
f = 1/(b*cos(x) + a*sin(x))
Fpos = (-log(-a/b + tan(x/2) - sqrt(a**2 + b**2)/b)/sqrt(a**2 + b**2)
+log(-a/b + tan(x/2) + sqrt(a**2 + b**2)/b)/sqrt(a**2 + b**2))
F = Piecewise(
# XXX: The zoo case here is for a=b=0 so it should just be zoo or maybe
# it doesn't really need to be included at all given that the original
# integrand is really undefined in that case anyway.
(zoo*(-log(tan(x/2) - 1) + log(tan(x/2) + 1)), Eq(a, 0) & Eq(b, 0)),
(log(tan(x/2))/a, Eq(b, 0)),
(-I/(-I*b*sin(x) + b*cos(x)), Eq(a, -I*b)),
(I/(I*b*sin(x) + b*cos(x)), Eq(a, I*b)),
(Fpos, True),
)
assert integrate(f, x) == F
ap, bp = symbols('a, b', positive=True)
rep = {a: ap, b: bp}
assert integrate(f.subs(rep), x) == Fpos.subs(rep)
| 790c4cef5e61644bbb6c467db1b902a8c482ee4b | 298 | https://github.com/sympy/sympy.git | 321 | def test_issue_23718():
f = 1/(b*cos(x) + a*sin(x))
Fpos = (-log(-a/b + tan(x/2) - sqrt(a**2 + b**2)/b)/sqrt(a**2 + b**2)
+log(-a/b + tan(x/2) + sqrt(a**2 + b**2)/b)/sqrt(a**2 + b**2))
F = Piecewise(
# XXX: The zoo case here is for a=b=0 so it should just be zoo or maybe
# it doesn't really need to be included at all given that the original
# integrand is really undefined in that case anyway.
(zoo*(-log(tan(x/2) - 1) + log(tan(x/2) + 1)), Eq(a, 0) & Eq(b, 0)),
(log(tan(x/2))/a, Eq(b, 0)),
(-I/(-I*b*sin( | 23 | 457 | test_issue_23718 |
|
145 | 0 | 8 | 32 | rllib/algorithms/pg/pg.py | 135,811 | [RLlib] Move all config validation logic into AlgorithmConfig classes. (#29854) | ray | 16 | Python | 91 | pg.py | def validate(self) -> None:
# Call super's validation method.
super().validate()
# Check for mismatches between `train_batch_size` and
# `rollout_fragment_length` (if not "auto")..
# Note: Only check this if `train_batch_size` > 0 (DDPPO sets this
# to -1 to auto-calculate the actual batch size later).
if (
self.rollout_fragment_length != "auto"
and not self.in_evaluation
and self.train_batch_size > 0
):
min_batch_size = (
max(self.num_rollout_workers, 1)
* self.num_envs_per_worker
* self.rollout_fragment_length
)
batch_size = min_batch_size
while batch_size < self.train_batch_size:
batch_size += min_batch_size
if (
batch_size - self.train_batch_size > 0.1 * self.train_batch_size
or batch_size - min_batch_size - self.train_batch_size
> (0.1 * self.train_batch_size)
):
suggested_rollout_fragment_length = self.train_batch_size // (
self.num_envs_per_worker * (self.num_rollout_workers or 1)
)
raise ValueError(
f"Your desired `train_batch_size` ({self.train_batch_size}) or a "
"value 10% off of that cannot be achieved with your other "
f"settings (num_rollout_workers={self.num_rollout_workers}; "
f"num_envs_per_worker={self.num_envs_per_worker}; "
f"rollout_fragment_length={self.rollout_fragment_length})! "
"Try setting `rollout_fragment_length` to 'auto' OR "
f"{suggested_rollout_fragment_length}."
)
| 2ed09c54459cc3f74e2dab13406018698559856c | 136 | https://github.com/ray-project/ray.git | 616 | def validate(self) -> None:
# Call super's validation method.
super().validate()
# Check for mismatches between `train_batch_size` and
# `rollout_fragment_length` (if not "auto")..
# Note: Only check t | 13 | 251 | validate |
|
37 | 0 | 1 | 31 | keras/engine/training_generator_test.py | 271,688 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | keras | 11 | Python | 25 | training_generator_test.py | def test_evaluate_generator_method(self):
model = test_utils.get_small_mlp(
num_hidden=3, num_classes=4, input_dim=2
)
model.compile(
loss="mse",
optimizer=rmsprop.RMSprop(1e-3),
metrics=["mae", metrics_module.CategoricalAccuracy()],
run_eagerly=test_utils.should_run_eagerly(),
)
model.evaluate_generator(
custom_generator_threads(),
steps=5,
max_queue_size=10,
workers=2,
verbose=1,
use_multiprocessing=True,
)
model.evaluate_generator(
custom_generator(),
steps=5,
max_queue_size=10,
use_multiprocessing=False,
)
model.evaluate_generator(
custom_generator(),
steps=5,
max_queue_size=10,
use_multiprocessing=False,
workers=0,
)
| 84afc5193d38057e2e2badf9c889ea87d80d8fbf | 138 | https://github.com/keras-team/keras.git | 326 | def test_evaluate_generator_method(self):
model = test_utils.get_small_mlp(
num_hidden=3, num_classes=4, input_dim=2
)
model.compile(
loss="mse",
optimizer=rmsprop.RMSprop(1e-3),
metrics=["mae", metrics_module.CategoricalAccuracy()],
run_eagerly=test_utils.should_run_eagerly(),
)
model.evaluate_generator(
custom_generator_threads(),
steps=5,
max_queue_size=10,
workers=2,
verbose=1,
use_multiprocessing=True,
) | 26 | 200 | test_evaluate_generator_method |
|
91 | 0 | 1 | 19 | sympy/printing/tests/test_pycode.py | 196,442 | printing: ArrayExpr support
Better support for numpy-style arrays in `TensorflowPrinter` and
`NumPyPrinter`. Printing methods are now collected in the
`ArrayPrinter` class to avoid code duplications/maintainance errors.
Printing for `ZeroArray` and `OneArray` has been added.
`ArrayDiagonal` printing now also works for multiple diagonals and
diagonals spanning more than two indices.
`ArrayContractiong` printing now also works when its base is not a
`ArrayTensorProduct`. | sympy | 11 | Python | 37 | test_pycode.py | def test_array_printer():
A = ArraySymbol('A', (4,4,6,6,6))
I = IndexedBase('I')
prntr = NumPyPrinter()
assert prntr.doprint(ZeroArray(5)) == 'numpy.zeros((5,))'
assert prntr.doprint(OneArray(5)) == 'numpy.ones((5,))'
assert prntr.doprint(ArrayContraction(A, [2,3])) == 'numpy.einsum("abccd->abd", A)'
assert prntr.doprint(I) == 'I'
assert prntr.doprint(ArrayDiagonal(A, [2,3,4])) == 'numpy.einsum("abccc->abc", A)'
assert prntr.doprint(ArrayDiagonal(A, [0,1], [2,3])) == 'numpy.einsum("aabbc->cab", A)'
assert prntr.doprint(ArrayContraction(A, [2], [3])) == 'numpy.einsum("abcde->abe", A)'
prntr = TensorflowPrinter()
assert prntr.doprint(ZeroArray(5)) == 'tensorflow.zeros((5,))'
assert prntr.doprint(OneArray(5)) == 'tensorflow.ones((5,))'
assert prntr.doprint(ArrayContraction(A, [2,3])) == 'tensorflow.linalg.einsum("abccd->abd", A)'
assert prntr.doprint(I) == 'I'
assert prntr.doprint(ArrayDiagonal(A, [2,3,4])) == 'tensorflow.linalg.einsum("abccc->abc", A)'
assert prntr.doprint(ArrayDiagonal(A, [0,1], [2,3])) == 'tensorflow.linalg.einsum("aabbc->cab", A)'
assert prntr.doprint(ArrayContraction(A, [2], [3])) == 'tensorflow.linalg.einsum("abcde->abe", A)'
| 8fe2c879fe862d9ab6547130e4ff65010eecb549 | 268 | https://github.com/sympy/sympy.git | 144 | def test_array_printer():
A = ArraySymbol('A', (4,4,6,6,6))
I = IndexedBase('I')
prntr = NumPyPrinter()
assert prntr.doprint(ZeroArray(5)) == 'numpy | 13 | 427 | test_array_printer |
|
21 | 0 | 4 | 6 | keras/engine/data_adapter.py | 271,122 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | keras | 10 | Python | 17 | data_adapter.py | def _is_list_of_scalars(inp):
if isinstance(inp, (float, int, str, bytes, bytearray)):
return True
if isinstance(inp, (list, tuple)) and inp:
return ListsOfScalarsDataAdapter._is_list_of_scalars(inp[0])
return False
| 84afc5193d38057e2e2badf9c889ea87d80d8fbf | 51 | https://github.com/keras-team/keras.git | 63 | def _is_list_of_scalars(inp):
if isinstance(inp, (float, | 11 | 73 | _is_list_of_scalars |
|
42 | 0 | 1 | 10 | tests/integration_tests/test_torchscript.py | 8,778 | Fix TorchText version in tokenizers ahead of torch 1.13.0 upgrade (#2838)
* fix torchtext version in tokenizers ahead of torch 1.13.0 upgrade
* add truncation test to torchscript
* check version before adding hf tokenizer to triton test
* revert triton in case the changes affected tests?
* cleanup | ludwig | 13 | Python | 36 | test_torchscript.py | def test_torchscript_e2e_text_hf_tokenizer_truncated_sequence(tmpdir, csv_filename):
data_csv_path = os.path.join(tmpdir, csv_filename)
input_features = [text_feature(encoder={"vocab_size": 3, "type": "bert"}, preprocessing={"max_sequence_length": 3})]
output_features = [
text_feature(decoder={"vocab_size": 3}),
]
backend = LocalTestBackend()
config = {"input_features": input_features, "output_features": output_features, TRAINER: {"epochs": 2}}
training_data_csv_path = generate_data(input_features, output_features, data_csv_path)
validate_torchscript_outputs(tmpdir, config, backend, training_data_csv_path)
| 51e763580a130801e4af64221614777761d8b364 | 104 | https://github.com/ludwig-ai/ludwig.git | 72 | def test_torchscript_e2e_text_hf_tokenizer_truncated_sequence(tmpdir, csv_filename):
data_csv_path = os.path.join(tmpdir, csv_filename)
input_features = [text_feature(encoder={"vocab_size": 3, "type": "bert"}, preprocessing={"max_sequence_length": 3})]
output_features = [
text_feature(decoder={"vocab_size": 3}),
]
backend = LocalTestBackend()
config = {"input_features": input_features, "output_features": output_features, TRAINER: {"epochs": 2}}
training_data_csv_path = generate_data(input_features, output_features, d | 20 | 169 | test_torchscript_e2e_text_hf_tokenizer_truncated_sequence |
|
24 | 0 | 1 | 16 | keras/callbacks_test.py | 270,083 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | keras | 10 | Python | 23 | callbacks_test.py | def test_default_callbacks_no_warning(self):
# Test that without the callback no warning is raised
model = sequential.Sequential()
model.add(keras.layers.Dense(1))
model.compile(
"sgd", loss="mse", run_eagerly=test_utils.should_run_eagerly()
)
warning_messages = []
| 84afc5193d38057e2e2badf9c889ea87d80d8fbf | 119 | https://github.com/keras-team/keras.git | 76 | def test_default_callbacks_no_warning(self):
# Test that without the callback no warning is raised
model = seq | 15 | 81 | test_default_callbacks_no_warning |
|
37 | 0 | 1 | 16 | corporate/tests/test_stripe.py | 83,907 | typing: Access url via key "Location" instead of attribute "url".
This is a part of #18777.
Signed-off-by: Zixuan James Li <[email protected]> | zulip | 9 | Python | 21 | test_stripe.py | def test_redirect_for_billing_home(self) -> None:
user = self.example_user("iago")
self.login_user(user)
response = self.client_get("/billing/")
self.assertEqual(response.status_code, 302)
self.assertEqual("/upgrade/", response["Location"])
user.realm.plan_type = Realm.PLAN_TYPE_STANDARD_FREE
user.realm.save()
response = self.client_get("/billing/")
self.assertEqual(response.status_code, 200)
user.realm.plan_type = Realm.PLAN_TYPE_LIMITED
user.realm.save()
Customer.objects.create(realm=user.realm, stripe_customer_id="cus_123")
response = self.client_get("/billing/")
self.assertEqual(response.status_code, 302)
self.assertEqual("/upgrade/", response["Location"])
| c34ac1fcd428b469e85bcd3070938e4f59e60b18 | 145 | https://github.com/zulip/zulip.git | 141 | def test_redirect_for_billing_home(self) -> None:
user = self.example_user("iago")
self.login_u | 19 | 245 | test_redirect_for_billing_home |
|
8 | 0 | 1 | 2 | homeassistant/components/amcrest/camera.py | 310,599 | Migrate amcrest integration to new async API (#56294) | core | 9 | Python | 8 | camera.py | async def _async_get_motion_recording(self) -> bool:
return await self._api.async_is_record_on_motion_detection()
| 7781e308cd7b28c67b6cf339f9b115c7190456fe | 16 | https://github.com/home-assistant/core.git | 14 | async def _async_get_motion_recording(self) -> bool:
return await self._api.async_is_record_on_motion_detection()
| 5 | 28 | _async_get_motion_recording |
|
17 | 1 | 1 | 2 | pandas/tests/io/parser/dtypes/test_dtypes_basic.py | 164,083 | TST: Remove unused fixtures (#45692)
* TST: Remove unused fixtures
* Undo a removed fixture
* Add back other fixtures
* Undo a file
* Try undoing this?
* Revert "Try undoing this?"
This reverts commit 0e56cb04f5e8cb1f7b2ac4c5e6191485bb2fe1ab. | pandas | 8 | Python | 16 | test_dtypes_basic.py | def test_decimal_and_exponential(python_parser_only, numeric_decimal, thousands):
# GH#31920
decimal_number_check(python_parser_only, numeric_decimal, thousands)
@pytest.mark.parametrize("thousands", ["_", None])
@pytest.mark.parametrize("float_precision", [None, "legacy", "high", "round_trip"]) | f46df091df3afea25a273f491d1f6b2c7d20b32c | @pytest.mark.parametrize("thousands", ["_", None])
@pytest.mark.parametrize("float_precision", [None, "legacy", "high", "round_trip"]) | 17 | https://github.com/pandas-dev/pandas.git | 20 | def test_decimal_and_exponential(python_parser_only, numeric_decimal, thousands):
| 8 | 84 | test_decimal_and_exponential |
18 | 0 | 1 | 7 | tests/builtin_server/tests.py | 201,908 | Refs #33476 -- Reformatted code with Black. | django | 10 | Python | 15 | tests.py | def test_file_wrapper_uses_sendfile(self):
env = {"SERVER_PROTOCOL": "HTTP/1.0"}
handler = FileWrapperHandler(None, BytesIO(), BytesIO(), env)
handler.run(wsgi_app_file_wrapper)
self.assertTrue(handler._used_sendfile)
self.assertEqual(handler.stdout.getvalue(), b"")
self.assertEqual(handler.stderr.getvalue(), b"")
| 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | 72 | https://github.com/django/django.git | 59 | def test_file_wrapper_uses_sendfile(self):
env = {"SERVER_PROTOCOL": "HTTP/1.0"}
ha | 14 | 119 | test_file_wrapper_uses_sendfile |
|
189 | 0 | 4 | 19 | PyInstaller/building/api.py | 264,011 | building: delay merging of reference path and name in DEPENDENCY TOC entry
Within MERGE, do not combine the reference path and target file
name into a single string and store it as the destination name
(the first TOC element). Instead, store the target file name as
destination name (the first TOC element) and the reference path
into the source name (the second TOC element, which is otherwise
left unused for DEPENDENCY TOC entries).
Have the CArchive writer perform the final merge, before writing
the entry to the PKG file.
This ensures that the target name remains unchanged within the
TOC, making it subject of de-duplication codepaths and duplication
checks. Previously, an entry for DEPENDENCY may end up duplicating
another entry (e.g., EXTENSION) at run-time, due to target name
containing the reference path prefix.
We can also get rid of DEPENDENCY-specific handling in `checkCache`
(which returns without any processing if `fnm` contains a colon);
this crutch was needed because `PKG.assemble` incorrectly handled
DEPENDENCY entries and unnecessarily tried running them through
`checkCache`. So we rework that part of `PKG.assemble` to process
DEPENDENCY entries as part of general entry handling. At this point,
this becomes necessary, because even if we kept the hack in
`checkCache`, there is no colon in the `fnm` anymore, so the check
would fail, leading to error... | pyinstaller | 15 | Python | 130 | api.py | def _process_toc(self, toc, path):
# NOTE: unfortunately, these need to keep two separate lists. See the comment in `_merge_dependencies` on why
# this is so.
toc_keep = []
toc_refs = []
for i, tpl in enumerate(toc):
if not tpl[1] in self._dependencies:
logger.debug("Adding dependency %s located in %s", tpl[1], path)
self._dependencies[tpl[1]] = path
# Add entry to list of kept TOC entries
toc_keep.append(tpl)
else:
dep_path = self._get_relative_path(path, self._dependencies[tpl[1]])
# Ignore references that point to the origin package. This can happen if the same resource is listed
# multiple times in TOCs (e.g., once as binary and once as data).
if dep_path.endswith(path):
logger.debug(
"Ignoring self-reference of %s for %s, located in %s - duplicated TOC entry?", tpl[1], path,
dep_path
)
# The entry is a duplicate, and should be ignored (i.e., do not add it to either of output TOCs).
continue
logger.debug("Referencing %s to be a dependency for %s, located in %s", tpl[1], path, dep_path)
# Create new DEPENDENCY entry; under destination path (first element), we store the original destination
# path, while source path contains the relative reference path.
toc_refs.append((tpl[0], dep_path, "DEPENDENCY"))
return toc_keep, toc_refs
# TODO: use pathlib.Path.relative_to() instead. | 8bd9c6726280aa0094c5e83ffcf31a0dbc7a0336 | 147 | https://github.com/pyinstaller/pyinstaller.git | 565 | def _process_toc(self, toc, path):
# NOTE: unfortunately, these need to keep two separate lists. See the comment in `_merge_dependencies` on why
# this is so.
toc_keep = []
toc_refs = []
for i, tpl in enumerate(toc):
if not tpl[1] in self._dependencies:
logger.debug("Adding dependency %s located in %s", tpl[1], path)
self._dependencies[tpl[1]] = path
# Add entry to list of kept TOC entries
toc_keep.append(tpl)
else:
dep_path = self._get_relative_path(path, self._dependencies[tpl[1]])
# Ignore references that point to the origin package. This can happen if the same resource is listed
# multiple times in TOCs (e.g., once as binary and once as data).
if dep_path.endswith(path):
logger.debug(
"Ignoring self-reference of %s for %s, located in %s - duplicated TOC entry?", tpl[1], path,
dep_path
)
# The entry is a duplicate, and should be ignored (i.e., do not add it to either of output TOCs).
continue
logger.debug("Referencing %s to be a dependency for %s, located in %s", tpl[1], path, dep_path)
# Create new DEPENDENCY entry; under destination path (first element), we store the original destination
# path, while source path contains the relative reference path.
toc_refs.append((tpl[0], dep_path, "DEPENDENCY"))
return toc_keep, toc_refs
# TODO: use pathlib.Path.relative_to() instead. | 16 | 236 | _process_toc |
|
22 | 0 | 3 | 7 | erpnext/hr/doctype/exit_interview/exit_interview.py | 66,109 | style: format code with black | erpnext | 12 | Python | 20 | exit_interview.py | def get_interviews(interviews):
import json
if isinstance(interviews, str):
interviews = json.loads(interviews)
if not len(interviews):
frappe.throw(_("Atleast one interview has to be selected."))
return interviews
| 494bd9ef78313436f0424b918f200dab8fc7c20b | 41 | https://github.com/frappe/erpnext.git | 15 | def get_interviews(interviews):
import json
if isinstance(interviews, str):
interviews = json.loads(interviews)
if not len(interviews):
frappe.throw(_("Atleast one interview has to be | 10 | 70 | get_interviews |
|
78 | 0 | 2 | 12 | pandas/tests/frame/indexing/test_indexing.py | 163,444 | DEPR: inconsistent series[i:j] slicing with Int64Index GH#45162 (#45324) | pandas | 11 | Python | 60 | test_indexing.py | def test_iloc_row_slice_view(self, using_array_manager):
df = DataFrame(np.random.randn(10, 4), index=range(0, 20, 2))
original = df.copy()
# verify slice is view
# setting it makes it raise/warn
subset = df.iloc[slice(4, 8)]
assert np.shares_memory(df[2], subset[2])
msg = r"\nA value is trying to be set on a copy of a slice from a DataFrame"
with pytest.raises(com.SettingWithCopyError, match=msg):
subset.loc[:, 2] = 0.0
exp_col = original[2].copy()
# TODO(ArrayManager) verify it is expected that the original didn't change
if not using_array_manager:
exp_col._values[4:8] = 0.0
tm.assert_series_equal(df[2], exp_col)
| 51675d0839480ba7ada44cc93ba8a8df94d33de0 | 135 | https://github.com/pandas-dev/pandas.git | 183 | def test_iloc_row_slice_view(self, using_array_manager):
df = DataFrame(np.random.randn(10, 4), index=range(0, 20, 2))
original = df.copy()
# verify slice is view
# setting it makes it raise/warn
subset = df.iloc[slice(4, 8)]
assert np.shares_memory(df[2], subset[2])
msg = r"\nA value is tryi | 27 | 202 | test_iloc_row_slice_view |
|
26 | 0 | 4 | 9 | django/db/models/sql/compiler.py | 205,821 | Refs #33476 -- Reformatted code with Black. | django | 10 | Python | 23 | compiler.py | def _expr_refs_base_model(cls, expr, base_model):
if isinstance(expr, Query):
return expr.model == base_model
if not hasattr(expr, "get_source_expressions"):
return False
return any(
cls._expr_refs_base_model(source_expr, base_model)
for source_expr in expr.get_source_expressions()
)
| 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | 54 | https://github.com/django/django.git | 97 | def _expr_refs_base_model(cls, expr, base_model):
if isinstance(expr, Query): | 11 | 83 | _expr_refs_base_model |
|
13 | 0 | 1 | 7 | erpnext/templates/pages/partners.py | 68,076 | style: format code with black | erpnext | 9 | Python | 13 | partners.py | def get_context(context):
partners = frappe.db.sql(
,
as_dict=True,
)
return {"partners": partners, "title": page_title}
| 494bd9ef78313436f0424b918f200dab8fc7c20b | 30 | https://github.com/frappe/erpnext.git | 7 | def get_context(context):
partners = frappe.db.sql(
,
as_dict=True,
)
return {"partners": partners, "title": p | 8 | 51 | get_context |
|
7 | 0 | 1 | 2 | asv_bench/benchmarks/benchmarks.py | 155,242 | TEST-#5261: port indexing, reindex and fillna benchmarks from pandas github (#5244)
Signed-off-by: arunjose696 <[email protected]>
Co-authored-by: Anatoly Myachev <[email protected]> | modin | 10 | Python | 7 | benchmarks.py | def time_getitem_slice(self, shape, index, index_structure):
execute(self.data[: self.index_to_query])
| 7c009c747caa90554607e30b9ac2bd1b190b8c7d | 23 | https://github.com/modin-project/modin.git | 13 | def time_getitem_slice(self, shape, index, index_structure):
execute(self.data[: self.index_to_query])
| 8 | 34 | time_getitem_slice |
|
19 | 0 | 2 | 4 | python3.10.4/Lib/encodings/punycode.py | 217,166 | add python 3.10.4 for windows | XX-Net | 11 | Python | 19 | punycode.py | def decode(self, input, final=False):
if self.errors not in ('strict', 'replace', 'ignore'):
raise UnicodeError("Unsupported error handling "+self.errors)
return punycode_decode(input, self.errors)
| 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 43 | https://github.com/XX-net/XX-Net.git | 43 | def decode(self, input, final=False):
if self.errors not in ('strict', 'replace', 'ignore'):
raise UnicodeError("Unsupported error handlin | 7 | 70 | decode |
|
69 | 0 | 1 | 2 | src/prefect/settings.py | 53,811 | Fix display of settings in api reference | prefect | 11 | Python | 58 | settings.py | def unreduce_settings(json):
return Settings.parse_raw(json)
# Dynamically create a pydantic model that includes all of our settings
SettingsFieldsMixin = create_model(
"SettingsFieldsMixin",
__base__=BaseSettings,
**{setting.name: (setting.type, setting.field) for setting in SETTINGS.values()},
)
# Defining a class after this that inherits the dynamic class rather than setting
# __base__ to the following class ensures that mkdocstrings properly generates
# reference documentation. It does support module-level variables, even if they have
# __doc__ set.
| 60e203e0eef82f49853fca133ed457f600044e8e | 13 | https://github.com/PrefectHQ/prefect.git | 77 | def unreduce_settings(json):
return Settings.parse_raw(json)
# Dynamically create a pydantic model that includes all of our settings
SettingsFieldsMixin = create_model(
"SettingsFieldsMixin",
__base__=BaseSettings,
**{setting.name: (setting.type, setting.field) f | 14 | 83 | unreduce_settings |
|
6 | 0 | 1 | 3 | apps/settings/models.py | 188,451 | Fix rbac (#7713)
* fix: token 系统用户增加 protocol
* fix: 修复清除orphan session时同时清除对应的 session_task
* perf: 修改 connection token api
* fix: 修复无法获取系统角色绑定的问题
* perf: 增加 db terminal 及 magnus 组件
* perf: 修改 migrations
* fix: 修复AUTHENTICATION_BACKENDS相关的逻辑
* fix: 修改判断backend认证逻辑
* fix: 修复资产账号查看密码跳过mfa
* fix: 修复用户组授权权限错误
* feat: 支持COS对象存储
* feat: 升级依赖 jms_storage==0.0.42
* fix: 修复 koko api 问题
* feat: 修改存储翻译信息
* perf: 修改 ticket 权限
* fix: 修复获取资产授权系统用户 get_queryset
* perf: 抽取 ticket
* perf: 修改 cmd filter 的权限
* fix: 修改 ticket perm
* fix: 修复oidc依赖问题
Co-authored-by: Eric <[email protected]>
Co-authored-by: ibuler <[email protected]>
Co-authored-by: 小冯 <[email protected]>
Co-authored-by: feng626 <[email protected]> | jumpserver | 8 | Python | 6 | models.py | def refresh_setting(self):
setattr(settings, self.name, self.cleaned_value)
self.refresh_keycloak_to_openid_if_need()
| 03afa4f9743fb8e6892be62a44b19dc48e0ed7f0 | 22 | https://github.com/jumpserver/jumpserver.git | 19 | def refresh_setting(self):
setattr(settings, self.name, self.cleaned_value)
self.refresh_keycloak_to_openid | 7 | 35 | refresh_setting |
|
13 | 0 | 1 | 11 | src/prefect/client.py | 55,904 | Block capabilities (PrefectHQ/orion#1898)
* Add capabilities to BlockSchemas
* Remove type field from BlockSchemas
* Create postgres migration, bump API version | prefect | 11 | Python | 13 | client.py | async def read_block_schemas(self) -> List[schemas.core.BlockSchema]:
response = await self._client.post(f"/block_schemas/filter", json={})
return pydantic.parse_obj_as(List[schemas.core.BlockSchema], response.json())
| 168483e9cf038a3629f880f838b5aa9291a48411 | 52 | https://github.com/PrefectHQ/prefect.git | 34 | async def read_block_schemas(self) -> List[schemas.core.BlockSchema]:
response = await self._client.post(f"/block_sc | 12 | 84 | read_block_schemas |
|
7 | 0 | 1 | 4 | wagtail/snippets/views/snippets.py | 77,938 | Add RevisionsCompare view in snippets | wagtail | 11 | Python | 7 | snippets.py | def history_label(self):
return _("{model_name} history").format(
model_name=self.model._meta.verbose_name
)
| e0a604e227efbaed6b072d17132e7ca806ef4948 | 23 | https://github.com/wagtail/wagtail.git | 31 | def history_label(self):
return _("{model_name} history").format(
model_name=self.model._meta.ver | 8 | 39 | history_label |
|
11 | 0 | 1 | 2 | doc/source/serve/doc_code/deploying_serve_example.py | 127,014 | [Serve][Doc] Update the doc code to use new api (#27689)
Co-authored-by: Archit Kulkarni <[email protected]> | ray | 7 | Python | 11 | deploying_serve_example.py | def hello(request):
return "hello world"
serve.run(hello.bind())
# __deploy_in_k8s_end__
subprocess.check_output(["ray", "stop", "--force"])
| 786c7f45cfb3495527894f81097712eb76f77e63 | 7 | https://github.com/ray-project/ray.git | 10 | def hello(request):
return "hello world"
serve.run(hello.bind | 7 | 55 | hello |
|
55 | 0 | 1 | 17 | python/ray/train/tests/test_predictor.py | 126,321 | [AIR - Datasets] Hide tensor extension from UDFs. (#27019)
We previously added automatic tensor extension casting on Datasets transformation outputs to allow the user to not have to worry about tensor column casting; however, this current state creates several issues:
1. Not all tensors are supported, which means that we’ll need to have an opaque object dtype (i.e. ndarray of ndarray pointers) fallback for the Pandas-only case. Known unsupported tensor use cases:
a. Heterogeneous-shaped (i.e. ragged) tensors
b. Struct arrays
2. UDFs will expect a NumPy column and won’t know what to do with our TensorArray type. E.g., torchvision transforms don’t respect the array protocol (which they should), and instead only support Torch tensors and NumPy ndarrays; passing a TensorArray column or a TensorArrayElement (a single item in the TensorArray column) fails.
Implicit casting with object dtype fallback on UDF outputs can make the input type to downstream UDFs nondeterministic, where the user won’t know if they’ll get a TensorArray column or an object dtype column.
3. The tensor extension cast fallback warning spams the logs.
This PR:
1. Adds automatic casting of tensor extension columns to NumPy ndarray columns for Datasets UDF inputs, meaning the UDFs will never have to see tensor extensions and that the UDF input column types will be consistent and deterministic; this fixes both (2) and (3).
2. No longer implicitly falls back to an opaque object dtype when TensorArray casting fails (e.g. for ragged tensors), and instead raises an error; this fixes (4) but removes our support for (1).
3. Adds a global enable_tensor_extension_casting config flag, which is True by default, that controls whether we perform this automatic casting. Turning off the implicit casting provides a path for (1), where the tensor extension can be avoided if working with ragged tensors in Pandas land. Turning off this flag also allows the user to explicitly control their tensor extension casting, if they want to work with it in their UDFs in order to reap the benefits of less data copies, more efficient slicing, stronger column typing, etc. | ray | 12 | Python | 44 | test_predictor.py | def test_predict(convert_to_pandas_mock, convert_from_pandas_mock):
input = pd.DataFrame({"x": [1, 2, 3]})
expected_output = input * 4.0
convert_to_pandas_mock.return_value = input
convert_from_pandas_mock.return_value = expected_output
checkpoint = Checkpoint.from_dict(
{"factor": 2.0, PREPROCESSOR_KEY: DummyPreprocessor()}
)
predictor = DummyPredictor.from_checkpoint(checkpoint)
actual_output = predictor.predict(input)
pd.testing.assert_frame_equal(actual_output, expected_output)
# Ensure the proper conversion functions are called.
convert_to_pandas_mock.assert_called_once_with(input, False)
convert_from_pandas_mock.assert_called_once()
pd.testing.assert_frame_equal(
convert_from_pandas_mock.call_args[0][0], expected_output
)
assert convert_from_pandas_mock.call_args[1]["type"] == DataType.PANDAS
| df124d0ad58ea7189e88f9fe42c1ee377ade9c8d | 133 | https://github.com/ray-project/ray.git | 113 | def test_predict(convert_to_pandas_mock, convert_from_pandas_mock):
input = pd.DataFrame({"x": [1, 2, 3]})
| 25 | 204 | test_predict |
|
14 | 0 | 1 | 8 | tests/rest/media/v1/test_filepath.py | 247,366 | Add type hints to `tests/rest` (#12146)
* Add type hints to `tests/rest`
* newsfile
* change import from `SigningKey` | synapse | 10 | Python | 14 | test_filepath.py | def test_remote_media_thumbnail_legacy(self) -> None:
self.assertEqual(
self.filepaths.remote_media_thumbnail_rel_legacy(
"example.com", "GerZNDnDZVjsOtardLuwfIBg", 800, 600, "image/jpeg"
),
"remote_thumbnail/example.com/Ge/rZ/NDnDZVjsOtardLuwfIBg/800-600-image-jpeg",
)
| 7e91107be1a4287873266e588a3c5b415279f4c8 | 32 | https://github.com/matrix-org/synapse.git | 83 | def test_remote_media_thumbnail_legacy(self) -> None:
self.assertEqual(
| 5 | 56 | test_remote_media_thumbnail_legacy |
|
32 | 0 | 4 | 7 | airbyte-integrations/connectors/source-hubspot/source_hubspot/api.py | 3,378 | Source Hubspot: Some incremental CRM objects and engagements (#8887) | airbyte | 14 | Python | 24 | api.py | def _update_state(self, latest_cursor):
if latest_cursor:
new_state = max(latest_cursor, self._state) if self._state else latest_cursor
if new_state != self._state:
logger.info(f"Advancing bookmark for {self.name} stream from {self._state} to {latest_cursor}")
self._state = new_state
self._start_date = self._state
| 25fb7e7fd744f3852ebe8152db5514513f8a2c9a | 52 | https://github.com/airbytehq/airbyte.git | 105 | def _update_state(self, latest_cursor):
if latest_cursor:
new_state = max(latest_cursor, self._state) if self._state else latest_cursor
if new_state != self._state:
logger.info(f"Advancing bookmark for {self.name} stream from {self._state} to {latest_cursor}")
self._state = new_state
self._start_date = self._state
| 10 | 99 | _update_state |
|
7 | 0 | 1 | 3 | python/ray/tune/utils/resource_updater.py | 146,811 | [Tune] Move resource updater out of trial executor (#23178)
* simplify trial executor
* update test
* fix: proper resource update before initialization
* add test to BUILD
* add doc for resource updater | ray | 7 | Python | 7 | resource_updater.py | def get_num_cpus(self) -> int:
self.update_avail_resources()
return self._avail_resources.cpu
| cc1728120f7d49b0016d190971bc8056d3245c5d | 18 | https://github.com/ray-project/ray.git | 20 | def get_num_cpus(self) -> int:
self.update_avail_resources()
return self._avail_resources. | 6 | 30 | get_num_cpus |
|
58 | 0 | 1 | 21 | tests/sentry/api/endpoints/test_user_notification_fine_tuning.py | 100,143 | ref(tests): Remove `get_valid_response()` (#34822) | sentry | 12 | Python | 42 | test_user_notification_fine_tuning.py | def test_permissions(self):
new_user = self.create_user(email="[email protected]")
new_org = self.create_organization(name="New Org")
new_team = self.create_team(name="New Team", organization=new_org, members=[new_user])
new_project = self.create_project(
organization=new_org, teams=[new_team], name="New Project"
)
data = {str(new_org.id): 0}
self.get_error_response("me", "reports", status_code=403, **data)
assert not UserOption.objects.filter(
user=self.user, organization=new_org, key="reports"
).exists()
data = {str(new_project.id): 1}
self.get_error_response("me", "alerts", status_code=403, **data)
value = NotificationSetting.objects.get_settings(
ExternalProviders.EMAIL,
NotificationSettingTypes.ISSUE_ALERTS,
user=self.user,
project=new_project,
)
assert value == NotificationSettingOptionValues.DEFAULT
| 096b5511e244eecd8799b2a0324655207ce8985e | 178 | https://github.com/getsentry/sentry.git | 221 | def test_permissions(self):
new_user = self.create_user(email="[email protected]")
new_org = self.create_organization(name="New Org")
new_team = self.create_team(name="New Team", organization=new_org, members=[new_user])
new_project = self.create_project(
organization=new_org, teams=[new_team], name="New Project"
)
data = {str(new_org.id): 0}
self.get_error_response("me", "reports", status_code=403, **data)
assert not UserOption.objects.filter(
user=s | 36 | 283 | test_permissions |
|
386 | 0 | 1 | 83 | ivy_tests/test_core/test_container.py | 213,790 | renamed dev_str arg to dev for all methods. | ivy | 17 | Python | 79 | test_container.py | def test_container_structural_diff(dev, call):
# all different keys or shapes
container_0 = Container({'a': ivy.array([1], dev=dev),
'b': {'c': ivy.array([2], dev=dev), 'd': ivy.array([3], dev=dev)}})
container_1 = Container({'a': ivy.array([[4]], dev=dev),
'b': {'c': ivy.array([[[5]]], dev=dev), 'e': ivy.array([3], dev=dev)}})
container_diff = ivy.Container.structural_diff(container_0, container_1)
assert np.equal(ivy.to_numpy(container_diff.a.diff_0), np.array([1]))
assert np.equal(ivy.to_numpy(container_diff.a.diff_1), np.array([[4]]))
assert np.equal(ivy.to_numpy(container_diff.b.c.diff_0), np.array([2]))
assert np.equal(ivy.to_numpy(container_diff.b.c.diff_1), np.array([[[5]]]))
assert np.equal(ivy.to_numpy(container_diff.b.d.diff_0), np.array([3]))
assert np.equal(ivy.to_numpy(container_diff.b.e.diff_1), np.array([3]))
container_diff_diff_only = ivy.Container.structural_diff(container_0, container_1, mode='diff_only')
assert container_diff_diff_only.to_dict() == container_diff.to_dict()
container_diff_same_only = ivy.Container.structural_diff(container_0, container_1, mode='same_only')
assert container_diff_same_only.to_dict() == {}
# some different shapes
container_0 = Container({'a': ivy.array([1], dev=dev),
'b': {'c': ivy.array([2], dev=dev), 'd': ivy.array([3], dev=dev)}})
container_1 = Container({'a': ivy.array([4], dev=dev),
'b': {'c': ivy.array([[5]], dev=dev), 'd': ivy.array([6], dev=dev)}})
container_diff = ivy.Container.structural_diff(container_0, container_1)
assert np.equal(ivy.to_numpy(container_diff.a), np.array([1]))
assert np.equal(ivy.to_numpy(container_diff.b.c.diff_0), np.array([2]))
assert np.equal(ivy.to_numpy(container_diff.b.c.diff_1), np.array([5]))
assert np.equal(ivy.to_numpy(container_diff.b.d), np.array([3]))
container_diff_diff_only = ivy.Container.structural_diff(container_0, container_1, mode='diff_only')
assert 'a' not in container_diff_diff_only
assert 'b' in container_diff_diff_only
assert 'c' in container_diff_diff_only['b']
assert 'd' not in container_diff_diff_only['b']
container_diff_same_only = ivy.Container.structural_diff(container_0, container_1, mode='same_only')
assert 'a' in container_diff_same_only
assert 'b' in container_diff_same_only
assert 'c' not in container_diff_same_only['b']
assert 'd' in container_diff_same_only['b']
# all different keys
container_0 = Container({'a': ivy.array([1], dev=dev),
'b': {'c': ivy.array([2], dev=dev), 'd': ivy.array([3], dev=dev)}})
container_1 = Container({'e': ivy.array([4], dev=dev),
'f': {'g': ivy.array([5], dev=dev), 'h': ivy.array([6], dev=dev)}})
container_diff = ivy.Container.structural_diff(container_0, container_1)
assert np.equal(ivy.to_numpy(container_diff.a.diff_0), np.array([1]))
assert np.equal(ivy.to_numpy(container_diff.b.diff_0.c), np.array([2]))
assert np.equal(ivy.to_numpy(container_diff.b.diff_0.d), np.array([3]))
assert np.equal(ivy.to_numpy(container_diff.e.diff_1), np.array([4]))
assert np.equal(ivy.to_numpy(container_diff.f.diff_1.g), np.array([5]))
assert np.equal(ivy.to_numpy(container_diff.f.diff_1.h), np.array([6]))
container_diff_diff_only = ivy.Container.structural_diff(container_0, container_1, mode='diff_only')
assert container_diff_diff_only.to_dict() == container_diff.to_dict()
container_diff_same_only = ivy.Container.structural_diff(container_0, container_1, mode='same_only')
assert container_diff_same_only.to_dict() == {}
# some different keys
container_0 = Container({'a': ivy.array([1], dev=dev),
'b': {'c': ivy.array([2], dev=dev), 'd': ivy.array([3], dev=dev)}})
container_1 = Container({'a': ivy.array([4], dev=dev),
'b': {'c': ivy.array([5], dev=dev), 'e': ivy.array([6], dev=dev)}})
container_diff = ivy.Container.structural_diff(container_0, container_1)
assert np.equal(ivy.to_numpy(container_diff.a), np.array([1]))
assert np.equal(ivy.to_numpy(container_diff.b.c), np.array([2]))
assert np.equal(ivy.to_numpy(container_diff.b.d.diff_0), np.array([3]))
assert np.equal(ivy.to_numpy(container_diff.b.e.diff_1), np.array([6]))
container_diff_diff_only = ivy.Container.structural_diff(container_0, container_1, mode='diff_only')
assert 'a' not in container_diff_diff_only
assert 'b' in container_diff_diff_only
assert 'c' not in container_diff_diff_only['b']
assert 'd' in container_diff_diff_only['b']
assert 'e' in container_diff_diff_only['b']
container_diff_same_only = ivy.Container.structural_diff(container_0, container_1, mode='same_only')
assert 'a' in container_diff_same_only
assert 'b' in container_diff_same_only
assert 'c' in container_diff_same_only['b']
assert 'd' not in container_diff_same_only['b']
assert 'e' not in container_diff_same_only['b']
# all same
container_0 = Container({'a': ivy.array([1], dev=dev),
'b': {'c': ivy.array([2], dev=dev), 'd': ivy.array([3], dev=dev)}})
container_1 = Container({'a': ivy.array([4], dev=dev),
'b': {'c': ivy.array([5], dev=dev), 'd': ivy.array([6], dev=dev)}})
container_diff = ivy.Container.structural_diff(container_0, container_1)
assert np.equal(ivy.to_numpy(container_diff.a), np.array([1]))
assert np.equal(ivy.to_numpy(container_diff.b.c), np.array([2]))
assert np.equal(ivy.to_numpy(container_diff.b.d), np.array([3]))
container_diff_diff_only = ivy.Container.structural_diff(container_0, container_1, mode='diff_only')
assert container_diff_diff_only.to_dict() == {}
container_diff_same_only = ivy.Container.structural_diff(container_0, container_1, mode='same_only')
assert container_diff_same_only.to_dict() == container_diff.to_dict()
| d743336b1f3654cd0315f380f43eed4116997c1d | 1,556 | https://github.com/unifyai/ivy.git | 896 | def test_container_structural_diff(dev, call):
# all different keys or shapes
container_0 = Container({'a': ivy.array([1], dev=dev),
'b': {'c': ivy.array([2], dev=dev), 'd': ivy.array([3], dev=dev)}})
container_1 = Container({'a': ivy.array([[4]], dev=dev),
'b': {'c': ivy.array([[[5]]], dev=dev), 'e': ivy.array([3], dev=dev)}})
container_diff = ivy.Container.structural_diff(container_0, container_1)
assert np.equal(ivy.to_numpy(container_diff.a.diff_0), np.array([1]))
assert np.equal(ivy.to_numpy(container_diff.a.diff_1), np.array([[4]]))
assert np.equal(ivy.to_numpy(container_diff.b.c.diff_0), np.array([2]))
assert np.equal(ivy.to_numpy(container_diff.b.c.diff_1), np.array([[[5]]]))
assert np.equal(ivy.to_numpy(container_diff.b.d.diff_0), np.array([3]))
assert np.equal(ivy.to_numpy(container_diff.b.e.diff_1), np.array([3]))
container_diff_diff_only = ivy.Container.structural_diff(container_0, container_1, mode='diff_only')
assert container_diff_diff_only.to_dict() == container_diff.to_dict()
container_diff_same_only = ivy.Container.structural_diff(container_0, container_1, mode='same_only')
assert container_diff_same_only.to_dict() == {}
# some different shapes
container_0 = Container({'a': ivy.array([1], dev=dev),
'b': {'c': ivy.array([2], dev=dev), 'd': ivy.array([3], dev=dev)}})
container_1 = Container({'a': ivy.array([4], dev=dev),
'b': {'c': ivy.array([[5]], dev=dev), 'd': ivy.array([6], dev=dev)}})
container_diff = ivy.Container.structural_diff(container_0, container_1)
assert np.equal(ivy.to_numpy(container_diff.a), np.array([1]))
assert np.equal(ivy.to_numpy(container_diff.b.c.diff_0), np.array([2]))
assert np.equal(ivy.to_numpy(container_diff.b.c.diff_1), np.array([5]))
assert np.equal(ivy.to_numpy(container_diff.b.d), np.array([3]))
container_diff_diff_only = ivy.Container.structural_diff(container_0, container_1, mode='diff_only')
assert 'a' not in container_diff_diff_only
assert 'b' in container_diff_diff_only
assert 'c' in container_diff_diff_only['b']
assert 'd' not in container_diff_diff_only['b']
container_diff_same_only = ivy.Container.structural_diff(container_0, container_1, mode='same_only')
assert 'a' in container_diff_same_only
assert 'b' in container_diff_same_only
assert 'c' not in container_diff_same_only['b']
assert 'd' in container_diff_same_only['b']
# all different keys
container_0 = Container({'a': ivy.array([1], dev=dev),
'b': {'c': ivy.array([2], dev=dev), 'd': ivy.array([3], dev=dev)}})
container_1 = Container({'e': ivy.array([4], dev=dev),
'f': {'g': ivy.array([5], dev=dev), 'h': ivy.array([6], dev=dev)}})
container_diff = ivy.Container.structural_diff(container_0, container_1)
assert np.equal(ivy.to_numpy(container_diff.a.diff_0), np.array([1]))
assert np.equal(ivy.to_numpy(container_diff.b.diff_0.c), np.array([2]))
assert np.equal(ivy.to_numpy(container_diff.b.diff_0.d), np.array([3]))
assert np.equal(ivy.to_numpy(container_diff.e.diff_1), np.array([4]))
assert np.equal(ivy.to_numpy(container_diff.f.diff_1.g), np.array([5]))
assert np.equal(ivy.to_numpy(container_diff.f.diff_1.h), np.array([6]))
container_diff_diff_only = ivy.Container.structural_diff(container_0, container_1, mode='diff_only')
assert container_diff_diff_only.to_dict() == container_diff.to_dict()
container_diff_same_only = ivy.Container.structural_diff(container_0, container_1, mode='same_only')
assert container_diff_same_only.to_dict() == {}
# some different keys
container_0 = Container({'a': ivy.array([1], dev=dev),
| 27 | 2,474 | test_container_structural_diff |
|
27 | 0 | 1 | 8 | tests/test_api_validate.py | 187,151 | plugin.api.validate: implement ValidationError
- Implement `ValidationError`
- Inherit from `ValueError` to preserve backwards compatiblity
- Allow collecting multiple errors (AnySchema)
- Keep an error stack of parent `ValidationError`s or other exceptions
- Format error stack when converting error to string
- Raise `ValidationError` instead of `ValueError`
- Add error contexts where it makes sense
- Add schema names to error instances
- Add and update tests | streamlink | 11 | Python | 24 | test_api_validate.py | def test_parse_json(self):
assert validate(parse_json(), '{"a": ["b", true, false, null, 1, 2.3]}') == {"a": ["b", True, False, None, 1, 2.3]}
with self.assertRaises(ValueError) as cm:
validate(parse_json(), "invalid")
assert_validationerror(cm.exception, )
| 3d44da082b3ba202b9d0557bfd8ce747a1d7960c | 60 | https://github.com/streamlink/streamlink.git | 58 | def test_parse_json(self):
assert validate(parse_json(), '{"a": ["b", true, false, null, 1, 2.3]}') == {"a": ["b", True, False, None, 1, 2.3]}
with self.assertRaises(ValueError) as cm:
validate(parse_json(), | 9 | 99 | test_parse_json |
|
129 | 0 | 3 | 13 | python/ccxt/aax.py | 15,128 | add fetchdeposits | ccxt | 10 | Python | 84 | aax.py | def fetch_deposits(self, code=None, since=None, limit=None, params={}):
self.load_markets()
request = {
# status Not required - Deposit status, "1: pending,2: confirmed, 3:failed"
# currency: Not required - String Currency
# startTime Not required Integer Default: 90 days from current timestamp.
# endTime Not required Integer Default: present timestamp.
}
currency = None
if code is not None:
currency = self.currency(code)
request['currency'] = currency['id']
if since is not None:
request['startTime'] = since # default 90 days
response = self.privateGetAccountDeposits(self.extend(request, params))
# { "code": 1,
# "data": [{
# "currency": "USDT",
# "network": "USDT",
# "quantity": "19.000000000000",
# "txHash": "75eb2e5f037b025c535664c49a0f7cc8f601dae218a5f4fe82290ff652c43f3d",
# "address": "1GkB7Taf7uttcguKEb2DmmyRTnihskJ9Le",
# "status": "2",
# "createdTime": "2021-01-08T19:45:01.354Z",
# "updatedTime": "2021-01-08T20:03:05.000Z",
# }]
# "message": "success",
# "ts": 1573561743499
# }
deposits = self.safe_value(response, 'data', [])
return self.parse_transactions(deposits, code, since, limit)
| 9c7c3aab121a5e6be89197156432970625688a70 | 110 | https://github.com/ccxt/ccxt.git | 451 | def fetch_deposits(self, code=None, since=None, limit=None, params={}):
self.load_markets()
request = {
# status Not required - Deposit status, "1: pending,2: confirmed, 3:failed"
| 15 | 191 | fetch_deposits |
|
84 | 0 | 6 | 26 | plugins/dbms/db2/fingerprint.py | 123,567 | Fixing DeprecationWarning (logger.warn) | sqlmap | 15 | Python | 44 | fingerprint.py | def checkDbms(self):
if not conf.extensiveFp and Backend.isDbmsWithin(DB2_ALIASES):
setDbms(DBMS.DB2)
return True
logMsg = "testing %s" % DBMS.DB2
logger.info(logMsg)
result = inject.checkBooleanExpression("[RANDNUM]=(SELECT [RANDNUM] FROM SYSIBM.SYSDUMMY1)")
if result:
logMsg = "confirming %s" % DBMS.DB2
logger.info(logMsg)
result = inject.checkBooleanExpression("JULIAN_DAY(CURRENT DATE) IS NOT NULL")
if not result:
warnMsg = "the back-end DBMS is not %s" % DBMS.DB2
logger.warning(warnMsg)
return False
version = self._versionCheck()
if version:
Backend.setVersion(version)
setDbms("%s %s" % (DBMS.DB2, Backend.getVersion()))
else:
setDbms(DBMS.DB2)
return True
else:
warnMsg = "the back-end DBMS is not %s" % DBMS.DB2
logger.warning(warnMsg)
return False
| df4293473d2fb6e887e31522cab5aff95e201581 | 149 | https://github.com/sqlmapproject/sqlmap.git | 358 | def checkDbms(self):
if not conf.extensiveFp and Backend.isDbmsWithin(DB2_ALIASES):
setDbms(DBMS.DB2)
return True
logMsg = "testing %s" % DBMS.DB2
logger.info(logMsg)
result = inject.checkBooleanExpression("[RANDNUM]=(SELECT [RANDNUM] FROM SYSIBM.SYSDUMMY1)")
if result:
| 22 | 258 | checkDbms |
|
42 | 0 | 1 | 12 | netbox/dcim/tests/test_natural_ordering.py | 266,185 | Clean up tests | netbox | 10 | Python | 30 | test_natural_ordering.py | def setUpTestData(cls):
site = Site.objects.create(name='Test Site 1', slug='test-site-1')
manufacturer = Manufacturer.objects.create(name='Test Manufacturer 1', slug='test-manufacturer-1')
devicetype = DeviceType.objects.create(
manufacturer=manufacturer, model='Test Device Type 1', slug='test-device-type-1'
)
devicerole = DeviceRole.objects.create(
name='Test Device Role 1', slug='test-device-role-1', color='ff0000'
)
Device.objects.create(
device_type=devicetype, device_role=devicerole, name='Test Device 1', site=site
)
| d4a231585ac9a25d9739552d8c9e433dbf9398af | 99 | https://github.com/netbox-community/netbox.git | 130 | def setUpTestData(cls):
site = Site.objects.create(name='Test Site 1', slug='test-site-1')
manufacturer = Manufacturer.objects.create(name='Test Manufacturer 1', slug='test-manufacturer-1')
devicetype = DeviceType.objects.create(
manufacturer=manufacturer, model='Test Device Type 1', slug='test-device-type-1'
)
devicerole = DeviceRole.objects.create(
name='Test Devic | 19 | 166 | setUpTestData |
|
78 | 0 | 1 | 26 | tests/ludwig/benchmarking/test_resource_usage_tracker.py | 7,396 | adding hardware usage and software packages tracker (#2195)
* adding hardware usage and software packages tracker
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* removed stdout redirection to null during import
* reverting
* updated `tracker.py`
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* improved docstring style
* removing unnecessary `torch.cuda.synchronize()` call
* using the `multiprocessing` library instead of the `@processify` wrapper to spawn the `Tracker` monitor process
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* style changes
* adding s3fs to `requirements.txt`
* name change to `resource_usage_tracker.py`
* added test
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* tag name validation
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* flake8 updates
* fixed test file
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* update test file
* fixing empty utilization (due to very short experiment)
* added # noqa E402
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> | ludwig | 12 | Python | 59 | test_resource_usage_tracker.py | def test_resource_usage_tracker(tmpdir):
train_df = pd.DataFrame(np.random.normal(0, 1, size=(100, 3)), columns=["input_1", "input_2", "output_1"])
eval_df = pd.DataFrame(np.random.normal(0, 1, size=(20, 3)), columns=["input_1", "input_2", "output_1"])
config = {
"input_features": [{"name": "input_1", "type": "number"}, {"name": "input_2", "type": "number"}],
"output_features": [{"name": "output_1", "type": "number"}],
"combiner": {"type": "concat", "output_size": 14},
TRAINER: {"epochs": 1},
}
model = LudwigModel(config=config, backend="local")
with ResourceUsageTracker(tag="train", output_dir=tmpdir, logging_interval=0.05, num_examples=len(train_df)):
model.train(
dataset=train_df,
output_directory=tmpdir,
skip_save_training_description=True,
skip_save_training_statistics=True,
skip_save_model=True,
skip_save_progress=True,
skip_save_log=True,
skip_save_processed_input=True,
)
with ResourceUsageTracker(tag="evaluate", output_dir=tmpdir, logging_interval=0.05, num_examples=len(eval_df)):
model.evaluate(dataset=eval_df)
assert os.path.exists(os.path.join(tmpdir, "train_resource_usage_metrics.json"))
assert os.path.exists(os.path.join(tmpdir, "evaluate_resource_usage_metrics.json"))
shutil.rmtree(tmpdir)
| ae8de108e14111afef08a5e9c429bb19e368c0b3 | 286 | https://github.com/ludwig-ai/ludwig.git | 244 | def test_resource_usage_tracker(tmpdir):
train_df = pd.DataFrame(np.random.normal(0, 1, size=(100, 3)), columns=["input_1", "input_2", "output_1"])
eval_df = pd.DataFrame(np.random.normal(0, 1, size=(20, 3)), columns=["input_1", "input_2", "output_1"])
config = {
"input_features": [{"name": "input_1", "type": "number"}, {"name": "input_2", "type": "number"}],
"output_features": [{"name": "output_1", "typ | 38 | 464 | test_resource_usage_tracker |
|
83 | 0 | 11 | 81 | code/default/smart_router/local/ip_region.py | 219,351 | Roll back 4.6.8 from upgrade | XX-Net | 13 | Python | 61 | ip_region.py | def generate_db(self):
keeprange = (
'0.0.0.0/8', # 本地网络
'10.0.0.0/8', # 私有网络
'100.64.0.0/10', # 地址共享（运营商 NAT）
'127.0.0.0/8', # 环回地址
'169.254.0.0/16', # 链路本地
'172.16.0.0/12', # 私有网络
'192.0.0.0/24', # 保留地址（IANA）
'192.0.2.0/24', # TEST-NET-1
'192.88.99.0/24', # 6to4 中继
'192.168.0.0/16', # 私有网络
'198.18.0.0/15', # 网络基准测试
'198.51.100.0/24', # TEST-NET-2
'203.0.113.0/24', # TEST-NET-3
# 连续地址直到 IP 结束，特殊处理
# '224.0.0.0/4', #组播地址（D类）
# '240.0.0.0/4', #保留地址（E类）
)
keeplist = []
for iprange in keeprange:
ip, mask = iprange.split('/')
keeplist.append((utils.ip_string_to_num(ip), 32 - int(mask)))
mask_dict = dict((str(2 ** i), i) for i in range(8, 25))
| 42dde73cebb1d524b6adfcde69fd947ed9b2440b | 537 | https://github.com/XX-net/XX-Net.git | 487 | def generate_db(self):
keeprange = (
'0.0.0.0/8', # 本地网络
'10.0.0.0/8', # 私有网络
'100.64.0.0/10', # 地址共享（运营商 NAT）
'127.0.0.0/8', # 环回地址
'169.254.0.0/16', # 链路本地
'172.16.0.0/12', # 私有网络
'192.0. | 17 | 182 | generate_db |
|
12 | 0 | 2 | 4 | python/ray/runtime_env.py | 146,321 | [runtime env] Deletes the proto cache on RuntimeEnv (#22944)
Mainly the following things:
- This PR deletes the proto cache on RuntimeEnv, ensuring that the user's modification of RuntimeEnv can take effect in the Proto message.
- validate whole runtime env when serialize runtime_env.
- overload method `__setitem__` to parse and validate field when it has to modify. | ray | 9 | Python | 11 | runtime_env.py | def py_container_image(self) -> Optional[str]:
if not self.has_py_container():
return None
return self["container"].get("image", "")
| 0c5440ee724a9f2b0fd94b7e6055c5be71968a84 | 32 | https://github.com/ray-project/ray.git | 36 | def py_container_image(self) -> Optional[str]:
if not self.has_py_container():
return None
return self["container"].get("image", "")
| 6 | 56 | py_container_image |
|
99 | 0 | 2 | 28 | sklearn/ensemble/tests/test_bagging.py | 260,854 | MAINT rename and deprecate `base_estimator` in favor of `estimator` in ensemble classes (#23819)
Co-authored-by: Adrian Trujillo Duron <[email protected]>
Co-authored-by: Guillaume Lemaitre <[email protected]> | scikit-learn | 13 | Python | 80 | test_bagging.py | def test_oob_score_classification():
# Check that oob prediction is a good estimation of the generalization
# error.
rng = check_random_state(0)
X_train, X_test, y_train, y_test = train_test_split(
iris.data, iris.target, random_state=rng
)
for estimator in [DecisionTreeClassifier(), SVC()]:
clf = BaggingClassifier(
estimator=estimator,
n_estimators=100,
bootstrap=True,
oob_score=True,
random_state=rng,
).fit(X_train, y_train)
test_score = clf.score(X_test, y_test)
assert abs(test_score - clf.oob_score_) < 0.1
# Test with few estimators
warn_msg = (
"Some inputs do not have OOB scores. This probably means too few "
"estimators were used to compute any reliable oob estimates."
)
with pytest.warns(UserWarning, match=warn_msg):
clf = BaggingClassifier(
estimator=estimator,
n_estimators=1,
bootstrap=True,
oob_score=True,
random_state=rng,
)
clf.fit(X_train, y_train)
| 306608e622bb3fb55095a97405b9ef0f1ad901d9 | 151 | https://github.com/scikit-learn/scikit-learn.git | 364 | def test_oob_score_classification():
# Check that oob prediction is a good estimation of the generalization
# error.
rng = check_random_state(0)
X_train, X_test, y_train, y_test = train_test_split(
iris.data, iris.target, random_state=rng
)
for estimator in [DecisionTreeClassifier(), SVC()]:
clf = BaggingClassifier(
estimator=estimator,
n_estimators=100,
bootstrap=True,
oob_score=True,
random_state=rng,
).fit(X_train, y_train)
test_score = clf.score(X_test, y_test)
assert abs(test_score - clf.oob_score_) < 0.1
# Test with few estimators
warn_msg = (
"Some inputs do not have OOB scores. This probably means too few "
"estimators were used to compute any reliable oob estimates."
)
with pytest. | 30 | 227 | test_oob_score_classification |
|
106 | 0 | 2 | 40 | onnx/backend/test/case/node/if.py | 254,806 | Use Python type annotations rather than comments (#3962)
* These have been supported since Python 3.5.
ONNX doesn't support Python < 3.6, so we can use the annotations.
Diffs generated by https://pypi.org/project/com2ann/.
Signed-off-by: Gary Miguel <[email protected]>
* Remove MYPY conditional logic in gen_proto.py
It breaks the type annotations and shouldn't be needed.
Signed-off-by: Gary Miguel <[email protected]>
* Get rid of MYPY bool from more scripts
Signed-off-by: Gary Miguel <[email protected]>
* move Descriptors class above where its referenced in type annotation
Signed-off-by: Gary Miguel <[email protected]>
* fixes
Signed-off-by: Gary Miguel <[email protected]>
* remove extra blank line
Signed-off-by: Gary Miguel <[email protected]>
* fix type annotations
Signed-off-by: Gary Miguel <[email protected]>
* fix type annotation in gen_docs
Signed-off-by: Gary Miguel <[email protected]>
* fix Operators.md
Signed-off-by: Gary Miguel <[email protected]>
* fix TestCoverage.md
Signed-off-by: Gary Miguel <[email protected]>
* fix protoc-gen-mypy.py
Signed-off-by: Gary Miguel <[email protected]> | onnx | 12 | Python | 71 | if.py | def export_if() -> None:
# Given a bool scalar input cond.
# return constant tensor x if cond is True, otherwise return constant tensor y.
then_out = onnx.helper.make_tensor_value_info('then_out', onnx.TensorProto.FLOAT, [5])
else_out = onnx.helper.make_tensor_value_info('else_out', onnx.TensorProto.FLOAT, [5])
x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
y = np.array([5, 4, 3, 2, 1]).astype(np.float32)
then_const_node = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=['then_out'],
value=onnx.numpy_helper.from_array(x)
)
else_const_node = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=['else_out'],
value=onnx.numpy_helper.from_array(y)
)
then_body = onnx.helper.make_graph(
[then_const_node],
'then_body',
[],
[then_out]
)
else_body = onnx.helper.make_graph(
[else_const_node],
'else_body',
[],
[else_out]
)
if_node = onnx.helper.make_node(
'If',
inputs=['cond'],
outputs=['res'],
then_branch=then_body,
else_branch=else_body
)
cond = np.array(1).astype(bool)
res = x if cond else y
expect(if_node, inputs=[cond], outputs=[res], name='test_if',
opset_imports=[onnx.helper.make_opsetid("", 11)])
| 83fa57c74edfd13ddac9548b8a12f9e3e2ed05bd | 287 | https://github.com/onnx/onnx.git | 483 | def export_if() -> None:
# Given a bool scalar input cond.
# return constant tensor x if cond is True, otherwise return constant tensor y.
then_out = onnx.helper.make_tensor_value_info('then_out', onnx.TensorProto.FLOAT, [5])
else_out = onnx.helper.make_tensor_value_info('else_out', onnx.TensorProto.FLOAT, [5])
x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
y = np.array([5, 4, 3, 2, 1]).astyp | 35 | 443 | export_if |
|
45 | 0 | 1 | 11 | tests/api/common/test_mark_tasks.py | 43,987 | Use `DagRun.run_id` instead of `execution_date` when updating state of TIs(UI & REST API) (#18724)
We can now use run_id as well as execution_date to update states
of task instances
Co-authored-by: Tzu-ping Chung <[email protected]>
Co-authored-by: Ash Berlin-Taylor <[email protected]> | airflow | 10 | Python | 37 | test_mark_tasks.py | def test_set_running_dag_run_to_success(self):
date = self.execution_dates[0]
dr = self._create_test_dag_run(State.RUNNING, date)
middle_time = timezone.utcnow()
self._set_default_task_instance_states(dr)
altered = set_dag_run_state_to_success(dag=self.dag1, run_id=dr.run_id, commit=True)
# All except the SUCCESS task should be altered.
expected = self._get_num_tasks_with_starting_state(State.SUCCESS, inclusion=False)
assert len(altered) == expected
self._verify_dag_run_state(self.dag1, date, State.SUCCESS)
self._verify_task_instance_states(self.dag1, date, State.SUCCESS)
self._verify_dag_run_dates(self.dag1, date, State.SUCCESS, middle_time)
| 2b4bf7fe67fc656ceb7bdaad36453b7a5b83ef04 | 123 | https://github.com/apache/airflow.git | 121 | def test_set_running_dag_run_to_success(self):
date = self.execution_dates[0]
| 26 | 185 | test_set_running_dag_run_to_success |
|
339 | 1 | 14 | 50 | networkx/algorithms/smallworld.py | 176,777 | Use isort with pre-commit to enforce import guidelines (#5659)
* Add isort to pre-commit
* Run isort on all python files (except __init__.py ones) | networkx | 17 | Python | 216 | smallworld.py | def lattice_reference(G, niter=5, D=None, connectivity=True, seed=None):
import numpy as np
from networkx.utils import cumulative_distribution, discrete_sequence
local_conn = nx.connectivity.local_edge_connectivity
if len(G) < 4:
raise nx.NetworkXError("Graph has less than four nodes.")
# Instead of choosing uniformly at random from a generated edge list,
# this algorithm chooses nonuniformly from the set of nodes with
# probability weighted by degree.
G = G.copy()
keys, degrees = zip(*G.degree()) # keys, degree
cdf = cumulative_distribution(degrees) # cdf of degree
nnodes = len(G)
nedges = nx.number_of_edges(G)
if D is None:
D = np.zeros((nnodes, nnodes))
un = np.arange(1, nnodes)
um = np.arange(nnodes - 1, 0, -1)
u = np.append((0,), np.where(un < um, un, um))
for v in range(int(np.ceil(nnodes / 2))):
D[nnodes - v - 1, :] = np.append(u[v + 1 :], u[: v + 1])
D[v, :] = D[nnodes - v - 1, :][::-1]
niter = niter * nedges
# maximal number of rewiring attempts per 'niter'
max_attempts = int(nnodes * nedges / (nnodes * (nnodes - 1) / 2))
for _ in range(niter):
n = 0
while n < max_attempts:
# pick two random edges without creating edge list
# choose source node indices from discrete distribution
(ai, ci) = discrete_sequence(2, cdistribution=cdf, seed=seed)
if ai == ci:
continue # same source, skip
a = keys[ai] # convert index to label
c = keys[ci]
# choose target uniformly from neighbors
b = seed.choice(list(G.neighbors(a)))
d = seed.choice(list(G.neighbors(c)))
bi = keys.index(b)
di = keys.index(d)
if b in [a, c, d] or d in [a, b, c]:
continue # all vertices should be different
# don't create parallel edges
if (d not in G[a]) and (b not in G[c]):
if D[ai, bi] + D[ci, di] >= D[ai, ci] + D[bi, di]:
# only swap if we get closer to the diagonal
G.add_edge(a, d)
G.add_edge(c, b)
G.remove_edge(a, b)
G.remove_edge(c, d)
# Check if the graph is still connected
if connectivity and local_conn(G, a, b) == 0:
# Not connected, revert the swap
G.remove_edge(a, d)
G.remove_edge(c, b)
G.add_edge(a, b)
G.add_edge(c, d)
else:
break
n += 1
return G
@py_random_state(3)
@not_implemented_for("directed")
@not_implemented_for("multigraph") | 5c0b11afb4c0882a070d522ef3fa41482ba935d3 | @py_random_state(3)
@not_implemented_for("directed")
@not_implemented_for("multigraph") | 516 | https://github.com/networkx/networkx.git | 976 | def lattice_reference(G, niter=5, D=None, connectivity=True, seed=None):
import numpy as np
from networkx.utils import cumulative_distribution, discrete_sequence
local_conn = nx.connectivity.local_edge_connectivity
if len(G) < 4:
raise nx.NetworkXError("Graph has less than four nodes.")
# Instead of choosing uniformly at random from a generated edge list,
# this algorithm chooses nonuniformly from the set of nodes with
# probability weighted by degree.
G = G.copy()
keys, degrees = zip(*G.degree()) # keys, degree
cdf = cumulative_distribution(degrees) # cdf of degree
nnodes = len(G)
nedges = nx.number_of_edges(G)
if D is None:
D = np.zeros((nnodes, nnodes))
un = np.arange(1, nnodes)
um = np.arange(nnodes - 1, 0, -1)
u = np.append((0,), np.where(un < um, un, um))
for v in range(int(np.ceil(nnodes / 2))):
D[nnodes - v - 1, :] = np.append(u[v + 1 :], u[: v + 1])
D[v, :] = D[nnodes - v - 1, :][::-1]
niter = niter * nedges
# maximal number of rewiring attempts per 'niter'
max_attempts = int(nnodes * nedges / (nnodes * (nnodes - 1) / 2))
for _ in range(niter):
n = 0
while n < max_attempts:
# pick two random edges without creating edge list
# choose source node indices from discrete distribution
(ai, ci) = discrete_sequence(2, cdistribution=cdf, seed=seed)
if ai == ci:
continue # same source, skip
a = keys[ai] # convert index to label
c = keys[ci]
# choose target uniformly from neighbors
b = seed.choice(list(G.neighbors(a)))
d = seed.choice(list(G.neighbors(c)))
bi = keys.index(b)
di = keys.index(d)
if b in [a, c, d] or d in [a, b, c]:
continue # all vertices should be different
# don't create parallel edges
if (d not in G[a]) and (b not in G[c]):
if D[ai, bi] + D[ci, di] >= D[ai, ci] + D[bi, di]:
# only swap if we get closer to the diagonal
| 57 | 829 | lattice_reference |
69 | 1 | 1 | 16 | tests/t5/test_modeling_tf_t5.py | 36,213 | TF: add beam search tests (#16202) | transformers | 13 | Python | 62 | test_modeling_tf_t5.py | def test_beam_search_generate(self):
model = TFT5ForConditionalGeneration.from_pretrained("t5-small")
tokenizer = T5Tokenizer.from_pretrained("t5-small")
sentences = ["I really love my", "Translate English to German: the transformers are truly amazing"]
input_ids = tokenizer(sentences, return_tensors="tf", padding=True).input_ids
generation_kwargs = {
"bad_words_ids": [tokenizer("my").input_ids, tokenizer("ein schöner").input_ids],
"no_repeat_ngram_size": 3,
"do_sample": False,
"repetition_penalty": 2.2,
"num_beams": 4,
}
output_ids = model.generate(input_ids, **generation_kwargs)
output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
expected_output_string = ["Ich liebe es so sehr!", "die Transformatoren sind wirklich erstaunlich"]
self.assertListEqual(expected_output_string, output_strings)
@require_tf
@require_sentencepiece
@require_tokenizers | 204c54d411c2b4c7f31405203533a51632f46ab1 | @require_tf
@require_sentencepiece
@require_tokenizers | 122 | https://github.com/huggingface/transformers.git | 190 | def test_beam_search_generate(self):
model = TFT5ForConditionalGeneration.from_pretrained("t5-small" | 22 | 216 | test_beam_search_generate |
37 | 0 | 1 | 6 | tests/cli/test_profile.py | 57,678 | Refactor tests for clarity | prefect | 15 | Python | 30 | test_profile.py | def authorized_cloud(self):
# attempts to reach the Cloud 2 workspaces endpoint implies a good connection
# to Prefect Cloud as opposed to a hosted Prefect Orion instance
with respx.mock:
authorized = respx.get(
"https://mock-cloud.prefect.io/api/me/workspaces",
).mock(return_value=Response(200, json={}))
yield authorized
| a0b82ae203029e65ba4dad2a93e545960eaca6ab | 36 | https://github.com/PrefectHQ/prefect.git | 105 | def authorized_cloud(self):
# attempts to reach the Cloud 2 workspaces endpoint implies a good connection
# to Prefect Cloud as opposed to a hosted Prefect Orion instance
with respx.mock:
authorized = respx.get(
"https://mock-cloud.prefect.io/api/me/workspaces",
).mock(return_value=Response(200, json={}))
yield authorized
| 9 | 64 | authorized_cloud |
|
24 | 0 | 1 | 18 | test/test_pipeline_yaml.py | 257,130 | Change YAML version exception into a warning (#2385)
* Change exception into warning, add strict_version param, and remove compatibility between schemas
* Simplify update_json_schema
* Rename unstable into master
* Prevent validate_config from changing the config to validate
* Fix version validation and add tests
* Rename master into ignore
* Complete parameter rename
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> | haystack | 12 | Python | 21 | test_pipeline_yaml.py | def test_load_yaml_missing_version(tmp_path):
with open(tmp_path / "tmp_config.yml", "w") as tmp_file:
tmp_file.write(
)
with pytest.raises(PipelineConfigError, match="Validation failed") as e:
Pipeline.load_from_yaml(path=tmp_path / "tmp_config.yml")
assert "version" in str(e)
| 4eec2dc45ee60e8b8780aa4f956aea8ad3624da3 | 54 | https://github.com/deepset-ai/haystack.git | 69 | def test_load_yaml_missing_version(tmp_path):
with open(tmp_path / "tmp_config.yml", "w") as tmp_file:
tmp_file.write(
)
with pytest.raises(PipelineConfigError, match="Validation failed") as e:
Pipeline.load_from_yaml(path=tmp_path / "tmp_config.yml")
assert "version" in str(e)
| 14 | 103 | test_load_yaml_missing_version |
|
66 | 0 | 6 | 26 | sympy/tensor/tensor.py | 200,587 | TensMul._dedupe_indices: rename variable | sympy | 13 | Python | 44 | tensor.py | def _dedupe_indices(new, exclude):
exclude = set(exclude)
dums_new = set(get_dummy_indices(new))
conflicts = dums_new.intersection(exclude)
if len(conflicts) == 0:
return None
exclude.update(dums_new)
exclude_for_gen = [(i, None) for i in exclude]
gen = _IndexStructure._get_generator_for_dummy_indices(exclude_for_gen)
repl = {}
for d in conflicts:
if -d in repl.keys():
continue
newname = gen(d.tensor_index_type)
new_d = d.func(newname, *d.args[1:])
repl[d] = new_d
repl[-d] = -new_d
if len(repl) == 0:
return None
new_renamed = new._replace_indices(repl)
return new_renamed
| 3e01222efcf2cf445f441eddc71e1c8194cee216 | 148 | https://github.com/sympy/sympy.git | 257 | def _dedupe_indices(new, exclude):
exclude = set(exclude)
dums_new = set(get_dummy_indices(new))
conflicts = dums_new.intersection(exclude)
if len(confl | 25 | 240 | _dedupe_indices |
|
23 | 0 | 1 | 10 | tests/cli/test_deployment_preview.py | 56,900 | Fix path for deployments test files | prefect | 13 | Python | 23 | test_deployment_preview.py | def test_preview_works_for_unnamed_deployments(deployments_path):
result = invoke_and_assert(
[
"deployment",
"preview",
str(deployments_path / "single_unnamed_deployment.py"),
],
expected_output_contains="kind: Job",
)
assert "Preview for <unnamed deployment specification>" in result.stdout
| d97eb751d3d526bae64b9d9580c75ebc0623121f | 35 | https://github.com/PrefectHQ/prefect.git | 89 | def test_preview_works_for_unnamed_deployments(deployments_path):
result = invoke_and_assert(
[
"deployment",
"preview",
str(deployments_path / "single_unnamed_deployment.py"),
],
expected_ | 7 | 64 | test_preview_works_for_unnamed_deployments |
|
18 | 0 | 2 | 8 | python3.10.4/Lib/bdb.py | 221,122 | add python 3.10.4 for windows | XX-Net | 10 | Python | 17 | bdb.py | def clear_bpbynumber(self, arg):
try:
bp = self.get_bpbynumber(arg)
except ValueError as err:
return str(err)
bp.deleteMe()
self._prune_breaks(bp.file, bp.line)
return None
| 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 47 | https://github.com/XX-net/XX-Net.git | 82 | def clear_bpbynumber(self, arg):
try:
bp = self.get_b | 12 | 79 | clear_bpbynumber |
|
17 | 1 | 1 | 7 | tests/components/picnic/test_services.py | 290,579 | Add service for adding products to a Picnic order (#67877)
* Add Picnic services for searching products and adding products to the cart
* Improve the Picnic services implementation and add unit tests
* Fix pre-commit check issues
* Fix comments and example product name
* Remove search service, update add_product service schema
* Fix pylint suggestion
* Add more tests and removed unused code
* Remove code needed for the removed service, clean tests from obvious comments and add type hints
* Remove unused import
* Remove unnecessary comments and simplify getting the config entry id
Co-authored-by: Allen Porter <[email protected]>
* Don't use hass.data in tests, make device id mandatory for service
* Rewrite all service tests so using lru.cache is not needed
* Add test for uncovered line in _product_search()
* Require a config entry id as service parameter instead of device id
* Use explicit check in get_api_client() and raise HomeAssistantError
* Fix HomeAssistantError import, fix services tests
* Change HomeAssistantError to ValueError when config entry is not found
Co-authored-by: Allen Porter <[email protected]> | core | 11 | Python | 14 | test_services.py | def picnic_api_client():
with patch(
"homeassistant.components.picnic.create_picnic_client"
) as create_picnic_client_mock:
picnic_client_mock = create_picnic_api_client(UNIQUE_ID)
create_picnic_client_mock.return_value = picnic_client_mock
yield picnic_client_mock
@pytest.fixture | a848dc11556624f8ebf2a09aff7192b84ab4f66e | @pytest.fixture | 26 | https://github.com/home-assistant/core.git | 53 | def picnic_api_client():
with patch(
"homeassistant.components.picnic.create_picnic_client"
) as create_picnic_client_mock:
picnic_client_mock = create_picnic_api_client(UNIQUE_ID)
| 9 | 59 | picnic_api_client |
11 | 0 | 2 | 5 | docs/examples/introduction/stopwatch.py | 184,537 | fix for call_later and scroll_to_widget | textual | 11 | Python | 11 | stopwatch.py | def action_remove_stopwatch(self) -> None:
timers = self.query("#timers Stopwatch")
if timers:
timers.last().remove()
| c891f6b70a0e885d2afe9a02bebb40e4af2864a6 | 28 | https://github.com/Textualize/textual.git | 43 | def action_remove_stopwatch(self) -> None:
timers = self.query("#timers Stopwatch")
| 6 | 52 | action_remove_stopwatch |
|
21 | 0 | 1 | 13 | tests/openbb_terminal/common/behavioural_analysis/test_sentimentinvestor_view.py | 285,232 | Here we merge all API Refactor related branches (#2236)
* Update api.py
* Updated forex menu
* refactor ycrv command
* refactor ycrv command black
* refactor ecocal command
* Minh changes
* Adding space to test pushing
* title fix ecocal df
* get economic calendar annotation
* fix investingcom tests
* refactor index command
* refactor overview command
* give defaults to wsj view function args
* rename date args investincom
* refacto bigmac command
* fix ecocal typo
* refactor rtps command
* alphavantage gdp
* alphavantage gdp per capita
* alphavantage cpi
* alphavantage tyld
* alphavantage inf
* refactor macro command
* refactor macro command w helpers
* refactor treasury command
* fix macro on terminal
* treasury labels
* refactor maturities
* update treasury maturities doc strings
* refactor get economic calendar finhub
* refactor map command api
* display map filter choices
* route economy api to performance map
* route economy api to performance map
* display group choices on valuation command
* refactor performance and valuation commands
* refactor spectrum model and view
* add choices to spectrum controller
* delete image after view
* fix model tests finviz
* fix finciz view tests
* refactor futures
* fix some tests
* fix more tests
* fix controller test
* refactor fred series notes
* update fred notes docstring
* refacto fred series ids
* fix pred and qa when empty datasets
* refactor fred
* uncomment stuff
* refacto get series data
* fix some tests
* set defaults on args
* refactor fred yield curve
* black
* fix spell and remove ecocal names
* fix linting
* linting
* pylint fix
* change dangerous defaults
* Working through crypto fixes (#2256)
* Working through crypto fixes
* Continued adding crypto stuff
* Added crypto overview
* Added test fixes
* Added fixtures
* Fixed tests
* Fixed charting issue
* Removed broken APIs
* Final adjustments
* Added test fixes
* map get groups and get ycrv countries into old api
* exposed econdb helper funcs
* remove helpers
* refactor search indices
* linting
* refactor arg currency
* pylint from currency
* Started switching crpyto ascending to ascend
* Merging
* Portfolio model arguements, params, and docstring
* Refactored for etf commands (#2292)
* Refactored for etf commands
* Fixed tests
* Added load command
* Fixed menu
* Portfolio logic fixes
* Added econometrics (#2260)
* Added econometrics
* Fixed tests
* Simplified API
* Added test fixes
* Added test csv
* Allowed examples to be loaded
* Fund refactor (#2291)
* Fund refactor
* Changed fund_name and fund to name
* Changed ascending to ascend
* Stock menu refactoring for easier API usage (#2194)
* Stocks refactoring for easier API usage
* Linting
* Refactor newly added features
* Linting
* Fixing tests
* Refactor common files used by stocks menu
* Fixing flake8
* Fix linting and tests
* Linting
* Fix flake8
* refactor insider_data
* refactor mentions
* refactor watchlist
* refactor sentiment
* refactor sentiment
* fix yahoofinance tests
* refactor load and candle
* refactor get_news and display_news
* refactor stocks.ins.act
* candle default matplotlib
* fix yahoofinance_view tests
* fix ark model tests
* fix ark view tests
* fix business insider model
* fix business insider view
* refactor csimarket model
* fix tests csi market model
* update dd controller
* fix get suppliers tests
* fix dd controller tests
* fix finhub tests
* fix finviz tests
* fix fmp tests
* fix marketwatch tests
* corrected argument keywords in test_bt_model
* corrected argument keywords in test_bt_view
* refactor fa controller
* refactor marketwatch view
* refactor gov controller
* fix tests fa av
* fix tests elect
* fix dcf tests
* fix polygon tests
* fix fmp tests
* fix quiverquant tests
* fix yahoofinance fa tests
* fix more fa tests
* fix insider tests
* fix more tests
* fix more tests
* fix options tests
* fix stock gov tests
* fix tests test_ba_controller
* fix tests for test_finviz_compare_model.py
* fixed 2 tests
* fixed tests
* fixed tests
* fixed tests
* fixed tests
* fixed tests
* fixed tests
* fixed tests
* fixed tests
* fixed tests
* fixed tests
* fix final tests
* fixed tests
* fixed tests
* Fix tests
* black
* forgot to black tests
* fixed tests
* fixed tests
* fixed tests
* fixed tests
* flakefix
* Tests + code : Stocks / Discovery
* fix tests
* added recorder
* fixed tests
* fixed tests
* black
* black
* remove unused imports
* refactor display raw
* sia dicts fix
* pylint
* linting
* remove dangerous default
* fix tests
* fix beta model test
* black
* skip screener qa test
* change sector path to sectors
* update tests readme
* fix metric defaults
* black
* substitute lost ticker
* defaults cpic
* another round on sia
* refactor cramer
* reduce default tweets on sentiment
* refactor yf hist, corr, volume
* arkorders default
* refactor income, balance, cashflow
* refacto scorr, screener, getfinnhub
* refactor stockgrid
* ibkr refactor
* another round on stockgrid
* add dividens end point
* refactor discovery endpoints
* update docstrings with similar input
* refactor messages
* refactor ba
* refactor regioons
* refactor twitter sentiment
* refactor hist
* refactor regions
* give default to timeframe
* refactor bunch of defaults and arg names
* remove leftover imports
* refactor vwap
* let tests run
* fix tests
* fix stock tests
* fix stockanalysis tests
* flake
* MYPY
* Made important changes
* added fixes
* Fixed big issue
* Added fixes to tests
* fix qa tests
* fix tests
* fix 1 more test
* last stocks failing
* fix crypto test
Co-authored-by: Chavithra PARANA <[email protected]>
Co-authored-by: montezdesousa <[email protected]>
Co-authored-by: hjoaquim <[email protected]>
Co-authored-by: montezdesousa <[email protected]>
Co-authored-by: colin99d <[email protected]>
* fix portfolio tests
* change period to window
* update ca docstrings
* refactor get_similar_companies func
* Fixed
* Update CI
* Update CI 2
* Update CI 3
* Update dependencies
Co-authored-by: colin99d <[email protected]>
Co-authored-by: Colin Delahunty <[email protected]>
Co-authored-by: montezdesousa <[email protected]>
Co-authored-by: James Simmons <[email protected]>
Co-authored-by: Theodore Aptekarev <[email protected]>
Co-authored-by: minhhoang1023 <[email protected]>
Co-authored-by: jose-donato <[email protected]>
Co-authored-by: montezdesousa <[email protected]>
Co-authored-by: northern-64bit <[email protected]>
Co-authored-by: hjoaquim <[email protected]> | OpenBBTerminal | 10 | Python | 20 | test_sentimentinvestor_view.py | def test_display_trending_empty_df(mocker):
view = "openbb_terminal.common.behavioural_analysis.sentimentinvestor_view"
# MOCK GET_HISTORICAL
mocker.patch(
target=f"{view}.sentimentinvestor_model.get_trending",
return_value=pd.DataFrame(),
)
sentimentinvestor_view.display_trending(
start_date=datetime(2021, 12, 21),
hour=9,
number=10,
limit=10,
export="",
)
| 9e1a58e2dbedec4e4a9f9c2e32ddf091776c606b | 58 | https://github.com/OpenBB-finance/OpenBBTerminal.git | 87 | def test_display_trending_empty_df(mocker):
view = "o | 16 | 94 | test_display_trending_empty_df |
|
28 | 0 | 2 | 9 | src/prefect/agent.py | 59,966 | Agent: Add limit to control number of concurrent flow runs (#7361)
Co-authored-by: Thomas Pedersen <[email protected]>
Co-authored-by: Michael Adkins <[email protected]> | prefect | 11 | Python | 23 | agent.py | async def start(self):
self.started = True
self.task_group = anyio.create_task_group()
self.limiter = (
anyio.CapacityLimiter(self.limit) if self.limit is not None else None
)
self.client = get_client()
await self.client.__aenter__()
await self.task_group.__aenter__()
| 045492f4d2205a0029514f5f00ec7560c06059a8 | 65 | https://github.com/PrefectHQ/prefect.git | 87 | async def start(self):
self.started = True
self.task_group = anyio.create_task_group()
self.limiter = (
anyio.CapacityLimiter(self | 12 | 107 | start |
|
32 | 0 | 5 | 9 | lib/matplotlib/axis.py | 107,773 | Refactor handling of tick and ticklabel visiblity in Axis.clear()
This is a follow-up to #20826, which makes the exceptions from clearing
more explicit. | matplotlib | 11 | Python | 27 | axis.py | def _reset_major_tick_kw(self, keep_tick_and_label_visibility=False):
backup = {name: value for name, value in self._major_tick_kw.items()
if name in ['tick1On', 'tick2On', 'label1On', 'label2On']}
self._major_tick_kw.clear()
if keep_tick_and_label_visibility:
self._major_tick_kw.update(backup)
self._major_tick_kw['gridOn'] = (
mpl.rcParams['axes.grid'] and
mpl.rcParams['axes.grid.which'] in ('both', 'major'))
| 2357c92d87d96d519c8470776e76180e71663d0b | 87 | https://github.com/matplotlib/matplotlib.git | 125 | def _reset_major_tick_kw(self, keep_tick_and_label_visibility=False):
backup = {name: value for name, value in self._major_tick_kw.items()
if name in ['tick1On', 'tick2On', 'label1On', 'label2On']}
self._major_tick_kw.clear()
if keep_tick_and_label_visibility:
self._major_tick_kw.update(backup)
self._major_tick_kw['gridOn'] = (
mpl.rcParams[ | 12 | 150 | _reset_major_tick_kw |
|
246 | 0 | 1 | 134 | python/ccxt/async_support/bitbns.py | 15,728 | 1.67.89
[ci skip] | ccxt | 16 | Python | 161 | bitbns.py | def describe(self):
return self.deep_extend(super(bitbns, self).describe(), {
'id': 'bitbns',
'name': 'Bitbns',
'countries': ['IN'], # India
'rateLimit': 1000,
'certified': False,
'pro': False,
'version': 'v2',
# new metainfo interface
'has': {
'spot': True,
'margin': None,
'swap': False,
'future': False,
'option': False,
'cancelOrder': True,
'createOrder': True,
'fetchBalance': True,
'fetchDepositAddress': True,
'fetchDeposits': True,
'fetchFundingHistory': False,
'fetchFundingRate': False,
'fetchFundingRateHistory': False,
'fetchFundingRates': False,
'fetchIndexOHLCV': False,
'fetchIsolatedPositions': False,
'fetchLeverage': False,
'fetchMarkets': True,
'fetchMarkOHLCV': False,
'fetchMyTrades': True,
'fetchOHLCV': None,
'fetchOpenOrders': True,
'fetchOrder': True,
'fetchOrderBook': True,
'fetchPositions': False,
'fetchPositionsRisk': False,
'fetchPremiumIndexOHLCV': False,
'fetchStatus': True,
'fetchTicker': 'emulated',
'fetchTickers': True,
'fetchTrades': True,
'fetchWithdrawals': True,
'reduceMargin': False,
'setLeverage': False,
'setPositionMode': False,
},
'timeframes': {
},
'urls': {
'logo': 'https://user-images.githubusercontent.com/1294454/117201933-e7a6e780-adf5-11eb-9d80-98fc2a21c3d6.jpg',
'api': {
'www': 'https://bitbns.com',
'v1': 'https://api.bitbns.com/api/trade/v1',
'v2': 'https://api.bitbns.com/api/trade/v2',
},
'www': 'https://bitbns.com',
'referral': 'https://ref.bitbns.com/1090961',
'doc': [
'https://bitbns.com/trade/#/api-trading/',
],
'fees': 'https://bitbns.com/fees',
},
'api': {
'www': {
'get': [
'order/fetchMarkets',
'order/fetchTickers',
'order/fetchOrderbook',
'order/getTickerWithVolume',
'exchangeData/ohlc', # ?coin=${coin_name}&page=${page}
'exchangeData/orderBook',
'exchangeData/tradedetails',
],
},
'v1': {
'get': [
'platform/status',
'tickers',
'orderbook/sell/{symbol}',
'orderbook/buy/{symbol}',
],
'post': [
'currentCoinBalance/EVERYTHING',
'getApiUsageStatus/USAGE',
'getOrderSocketToken/USAGE',
'currentCoinBalance/{symbol}',
'orderStatus/{symbol}',
'depositHistory/{symbol}',
'withdrawHistory/{symbol}',
'withdrawHistoryAll/{symbol}',
'depositHistoryAll/{symbol}',
'listOpenOrders/{symbol}',
'listOpenStopOrders/{symbol}',
'getCoinAddress/{symbol}',
'placeSellOrder/{symbol}',
'placeBuyOrder/{symbol}',
'buyStopLoss/{symbol}',
'sellStopLoss/{symbol}',
'placeSellOrder/{symbol}',
'cancelOrder/{symbol}',
'cancelStopLossOrder/{symbol}',
'listExecutedOrders/{symbol}',
'placeMarketOrder/{symbol}',
'placeMarketOrderQnty/{symbol}',
],
},
'v2': {
'post': [
'orders',
'cancel',
'getordersnew',
'marginOrders',
],
},
},
'fees': {
'trading': {
'feeSide': 'quote',
'tierBased': False,
'percentage': True,
'taker': self.parse_number('0.0025'),
'maker': self.parse_number('0.0025'),
},
},
'exceptions': {
'exact': {
'400': BadRequest, # {"msg":"Invalid Request","status":-1,"code":400}
'409': BadSymbol, # {"data":"","status":0,"error":"coin name not supplied or not yet supported","code":409}
'416': InsufficientFunds, # {"data":"Oops ! Not sufficient currency to sell","status":0,"error":null,"code":416}
'417': OrderNotFound, # {"data":[],"status":0,"error":"Nothing to show","code":417}
},
'broad': {},
},
})
| 4e4e4e5d50f9a10f38d2aac5ea07696b84b365c4 | 434 | https://github.com/ccxt/ccxt.git | 2,545 | def describe(self):
return self.deep_extend(super(bitbns, self).describe(), {
'id': 'bitbns',
'name': 'Bitbns',
'countries': ['IN'], # India
'rateLimit': 1000,
'certified': False,
'pro': False,
'version': 'v2',
# new metainfo interface
'has': {
'spot': True,
'margin': None,
'swap': False,
'future': False,
'option': False,
'cancelOrder': True,
'createOrder': True,
'fetchBalance': True,
'fetchDepositAddress': True,
'fetchDeposits': True,
'fetchFundingHistory': False,
'fetchFundingRate': False,
'fetchFundingRateHistory': False,
'fetchFundingRates': False,
'fetchIndexOHLCV': False,
'fetchIsolatedPositions': False,
'fetchLeverage': False,
'fetchMarkets': True,
'fetchMarkOHLCV': False,
'fetchMyTrades': True,
'fetchOHLCV': None,
'fetchOpenOrders': True,
'fetchOrder': True,
'fetchOrderBook': True,
'fetchPositions': False,
'fetchPositionsRisk': False,
'fetchPremiumIndexOHLCV': False,
'fetchStatus': True,
'fetchTicker': 'emulated',
'fetchTickers': True,
'fetchTrades': True,
'fetchWithdrawals': True,
'reduceMargin': False,
'setLeverage': False,
'setPositionMode': False,
},
'timeframes': {
},
'urls': {
'logo': 'https://user-images.githubusercontent.com/1294454/117201933-e7a6e780-adf5-11eb-9d80-98fc2a21c3d6.jpg',
'api': {
'www': 'https://bitbns.com',
'v1': 'https://api.bitbns.com/api/trade/v1',
'v2': 'https://api.bitbns.com/api/trade/v2',
},
'www': 'https://bitbns.com',
'referral': 'https://ref.bitbns.com/1090961',
'doc': [
'https://bitbns.com/trade/#/api-trading/',
],
'fees': 'https://bitbns.com/fees',
},
'api': {
'www': {
'get': [
'order/fetchMarkets',
'order/fetchTickers',
'order/fetchOrderbook',
'order/getTickerWit | 10 | 814 | describe |
|
52 | 0 | 1 | 5 | python3.10.4/Lib/ctypes/test/test_memfunctions.py | 222,071 | add python 3.10.4 for windows | XX-Net | 12 | Python | 36 | test_memfunctions.py | def test_overflow(self):
# string_at and wstring_at must use the Python calling
# convention (which acquires the GIL and checks the Python
# error flag). Provoke an error and catch it; see also issue
# #3554: <http://bugs.python.org/issue3554>
self.assertRaises((OverflowError, MemoryError, SystemError),
lambda: wstring_at(u"foo", sys.maxint - 1))
self.assertRaises((OverflowError, MemoryError, SystemError),
lambda: string_at("foo", sys.maxint - 1))
| 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 56 | https://github.com/XX-net/XX-Net.git | 144 | def test_overflow(self):
# string_at and wstring_at must use the Python calling
# convention (which acquires the GIL and checks the Python
# error flag). Provoke an error and catch it; see also issue
# #3554: <http://b | 10 | 87 | test_overflow |
|
15 | 0 | 1 | 6 | trainer/craft/data/dataset.py | 122,991 | add CRAFT training code | EasyOCR | 10 | Python | 13 | dataset.py | def resize_to_half(self, ground_truth, interpolation):
return cv2.resize(
ground_truth,
(self.output_size // 2, self.output_size // 2),
interpolation=interpolation,
)
| f50a6a2867b77250cbd375217d0f7f32297891d8 | 26 | https://github.com/JaidedAI/EasyOCR.git | 61 | def resize_to_half(self, ground_truth, interpolation):
return cv2.resize(
ground_truth,
(self.output_size // 2, self | 7 | 49 | resize_to_half |
|
8 | 0 | 1 | 4 | tests/sentry/integrations/slack/test_requests.py | 91,371 | ref: replace self.assertRaises with pytest.raises (#35685)
* add flake8 plugin to detect assertRaises
* ref: replace self.assertRaises with pytest.raises
* non-sed fixes | sentry | 10 | Python | 8 | test_requests.py | def test_validate_missing_event_type(self):
self.request.data["event"] = {}
with pytest.raises(SlackRequestError):
self.slack_request.validate()
| 284e980df0018f8baee659999268bdd4c7d08255 | 31 | https://github.com/getsentry/sentry.git | 32 | def test_validate_missing_event_type(self):
self.request.data["ev | 9 | 55 | test_validate_missing_event_type |
|
194 | 0 | 8 | 72 | homeassistant/components/sensibo/coordinator.py | 312,482 | Bugfix temp step list out of range sensibo (#65782) | core | 15 | Python | 153 | coordinator.py | async def _async_update_data(self) -> dict[str, dict[str, Any]]:
devices = []
try:
for dev in await self.client.async_get_devices():
devices.append(dev)
except (pysensibo.SensiboError) as error:
raise UpdateFailed from error
device_data: dict[str, dict[str, Any]] = {}
for dev in devices:
unique_id = dev["id"]
name = dev["room"]["name"]
temperature = dev["measurements"].get("temperature", 0.0)
humidity = dev["measurements"].get("humidity", 0)
ac_states = dev["acState"]
target_temperature = ac_states.get("targetTemperature")
hvac_mode = ac_states.get("mode")
running = ac_states.get("on")
fan_mode = ac_states.get("fanLevel")
swing_mode = ac_states.get("swing")
available = dev["connectionStatus"].get("isAlive", True)
capabilities = dev["remoteCapabilities"]
hvac_modes = list(capabilities["modes"])
if hvac_modes:
hvac_modes.append("off")
current_capabilities = capabilities["modes"][ac_states.get("mode")]
fan_modes = current_capabilities.get("fanLevels")
swing_modes = current_capabilities.get("swing")
temperature_unit_key = dev.get("temperatureUnit") or ac_states.get(
"temperatureUnit"
)
temperatures_list = (
current_capabilities["temperatures"]
.get(temperature_unit_key, {})
.get("values", [0, 1])
)
if temperatures_list:
temperature_step = temperatures_list[1] - temperatures_list[0]
features = list(ac_states)
state = hvac_mode if hvac_mode else "off"
fw_ver = dev["firmwareVersion"]
fw_type = dev["firmwareType"]
model = dev["productModel"]
calibration_temp = dev["sensorsCalibration"].get("temperature", 0.0)
calibration_hum = dev["sensorsCalibration"].get("humidity", 0.0)
device_data[unique_id] = {
"id": unique_id,
"name": name,
"ac_states": ac_states,
"temp": temperature,
"humidity": humidity,
"target_temp": target_temperature,
"hvac_mode": hvac_mode,
"on": running,
"fan_mode": fan_mode,
"swing_mode": swing_mode,
"available": available,
"hvac_modes": hvac_modes,
"fan_modes": fan_modes,
"swing_modes": swing_modes,
"temp_unit": temperature_unit_key,
"temp_list": temperatures_list,
"temp_step": temperature_step,
"features": features,
"state": state,
"fw_ver": fw_ver,
"fw_type": fw_type,
"model": model,
"calibration_temp": calibration_temp,
"calibration_hum": calibration_hum,
}
return device_data
| 07edbc42a48a4ccedab660ec20fa0e93fe79ad46 | 454 | https://github.com/home-assistant/core.git | 1,071 | async def _async_update_data(self) -> dict[str, dict[str, Any]]:
devices = []
try:
for dev in await self.client.async_get_devices():
devices.append(dev)
except (pysensibo.SensiboError) as error:
raise UpdateFailed from error
device_data: dict[str, dict[str, Any]] = {}
for dev in devices:
unique_id = dev["id"]
name = dev["room"]["name"]
temperature = dev["measurements"].get("temperature", 0.0)
| 43 | 775 | _async_update_data |
|
28 | 0 | 2 | 13 | label_studio/labels_manager/api.py | 177,694 | feat: DEV-1926: Add labels api (#2128)
* feat: DEV-1926: Add labels api
* Update DM to master branch
Co-authored-by: hlomzik <[email protected]> | label-studio | 12 | Python | 25 | api.py | def post(self, request):
serializer = LabelBulkUpdateSerializer(data=request.data)
serializer.is_valid(raise_exception=True)
project = serializer.validated_data['project']
if project is not None:
self.check_object_permissions(self.request, project)
updated_count = bulk_update_label(
old_label=serializer.validated_data['old_label'],
new_label=serializer.validated_data['new_label'],
organization=self.request.user.active_organization,
project=project,
)
return Response({'annotations_updated': updated_count})
| 03bd7e0238b7c21d6276e0b927a1722ed7c0aedc | 95 | https://github.com/heartexlabs/label-studio.git | 131 | def post(self, request):
serializer = LabelBulkUpdateSerializer(data=request.data)
serializer.is_valid(raise_exception=True)
project = serializer.validated_data['project']
if project is not | 19 | 150 | post |
|
21 | 0 | 1 | 15 | tests/providers/google/cloud/hooks/test_dataplex.py | 47,006 | Fix new MyPy errors in main (#22884)
Those MyPe errors are side effect of some new dependencies. | airflow | 11 | Python | 21 | test_dataplex.py | def test_list_tasks(self, mock_client):
self.hook.list_tasks(project_id=PROJECT_ID, region=REGION, lake_id=LAKE_ID)
parent = f'projects/{PROJECT_ID}/locations/{REGION}/lakes/{LAKE_ID}'
mock_client.return_value.list_tasks.assert_called_once_with(
request=dict(
parent=parent,
page_size=None,
page_token=None,
filter=None,
order_by=None,
),
retry=DEFAULT,
timeout=None,
metadata=(),
)
| 6933022e94acf139b2dea9a589bb8b25c62a5d20 | 77 | https://github.com/apache/airflow.git | 178 | def test_list_tasks(self, mock_client):
self.hook.list_tasks(project_id=PROJECT_ID, region=REGION, lake_id=LAKE_ID)
| 24 | 122 | test_list_tasks |
|
117 | 1 | 2 | 35 | tests/test_models.py | 214,576 | make `add_unk` optional and don't use it for ner | flair | 12 | Python | 93 | test_models.py | def test_sequence_tagger_transformer_finetune(results_base_path, tasks_base_path):
flair.set_seed(123)
# load dataset
corpus: Corpus = ColumnCorpus(
data_folder=tasks_base_path / "trivial" / "trivial_bioes",
column_format={0: "text", 1: "ner"},
)
tag_dictionary = corpus.make_label_dictionary("ner", add_unk=False)
# tagger without CRF
tagger: SequenceTagger = SequenceTagger(
hidden_size=64,
embeddings=TransformerWordEmbeddings("distilbert-base-uncased", fine_tune=True),
tag_dictionary=tag_dictionary,
tag_type="ner",
use_crf=False,
use_rnn=False,
reproject_embeddings=False,
)
# train
trainer = ModelTrainer(tagger, corpus)
trainer.fine_tune(
results_base_path,
mini_batch_size=2,
max_epochs=10,
shuffle=True,
learning_rate=0.5e-4,
)
loaded_model: SequenceTagger = SequenceTagger.load(results_base_path / "final-model.pt")
sentence = Sentence("this is New York")
sentence_empty = Sentence(" ")
loaded_model.predict(sentence)
loaded_model.predict([sentence, sentence_empty])
loaded_model.predict([sentence_empty])
# check if loaded model can predict
entities = [label.data_point.text for label in sentence.get_labels("ner")]
assert "New York" in entities
# check if loaded model successfully fit the training data
result: Result = loaded_model.evaluate(corpus.test, gold_label_type="ner")
assert result.classification_report["micro avg"]["f1-score"] == 1.0
del loaded_model
@pytest.mark.integration | 6ed3648502ddc7d44e8b6b3f9f8e6adcb15cf134 | @pytest.mark.integration | 231 | https://github.com/flairNLP/flair.git | 294 | def test_sequence_tagger_transformer_finetune(results_base_path, tasks_base_path):
flair.set_seed(123)
# load dataset
corpus: Corpus = ColumnCorpus(
data_folder=tasks_base_path / "trivial" / "trivial_bioes",
column_format={0: "text", 1: "ner"},
)
tag_dictionary = corpus.make_label_dictionary("ner", add_unk=False)
# tagger without CR | 49 | 377 | test_sequence_tagger_transformer_finetune |
20 | 0 | 1 | 10 | tests/basic/tests.py | 201,885 | Refs #33476 -- Reformatted code with Black. | django | 11 | Python | 19 | tests.py | def test_objects_attribute_is_only_available_on_the_class_itself(self):
with self.assertRaisesMessage(
AttributeError, "Manager isn't accessible via Article instances"
):
getattr(
Article(),
"objects",
)
self.assertFalse(hasattr(Article(), "objects"))
self.assertTrue(hasattr(Article, "objects"))
| 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | 48 | https://github.com/django/django.git | 110 | def test_objects_attribute_is_only_available_on_the_class_itself(self):
with self.assertRaisesMessage(
AttributeError, "Manager isn't accessible via Article instances"
):
getattr | 9 | 86 | test_objects_attribute_is_only_available_on_the_class_itself |
|
24 | 0 | 2 | 8 | tests/components/lcn/test_cover.py | 314,845 | Add tests for LCN sensor and binary_sensor platforms (#67263) | core | 11 | Python | 22 | test_cover.py | async def test_setup_lcn_cover(hass, entry, lcn_connection):
for entity_id in (
COVER_OUTPUTS,
COVER_RELAYS,
):
state = hass.states.get(entity_id)
assert state is not None
assert state.state == STATE_OPEN
| b7b8feda0ffb7487954545c96c50e7f64e2195bc | 41 | https://github.com/home-assistant/core.git | 68 | async def test_setup_lcn_cover(hass, entry, lcn_connection):
for entity_id in (
COVER_OUTPUTS,
COVER_RELAYS,
):
state = hass.states.get(entity_id)
assert state is not None
assert state.stat | 11 | 63 | test_setup_lcn_cover |
|
25 | 0 | 4 | 8 | src/sentry/integrations/jira/client.py | 86,766 | ref: type sentry/utils/assets.py and sentry/utils/http.py (#39624) | sentry | 11 | Python | 20 | client.py | def get_project_key_for_id(self, project_id) -> str:
if not project_id:
return ""
projects = self.get_projects_list()
for project in projects:
if project["id"] == project_id:
return project["key"]
return ""
| e9ce61066783c3601acd75fa74a9f4af6bd696c1 | 42 | https://github.com/getsentry/sentry.git | 89 | def get_project_key_for_id(self, project_id) -> str:
if not project_id:
return ""
projects = self.get_projects_list()
for project in projects:
if proj | 7 | 73 | get_project_key_for_id |
|
15 | 0 | 2 | 5 | wagtail/api/v2/tests/test_pages.py | 72,791 | Reformat with black | wagtail | 13 | Python | 14 | test_pages.py | def test_remove_id_field(self):
response = self.get_response(fields="-id")
content = json.loads(response.content.decode("UTF-8"))
for page in content["items"]:
self.assertEqual(set(page.keys()), {"meta", "title"})
| d10f15e55806c6944827d801cd9c2d53f5da4186 | 57 | https://github.com/wagtail/wagtail.git | 46 | def test_remove_id_field(self):
response = self.get_response(fields="-id")
content = json.loads(response.content.decode("UTF-8"))
for | 13 | 99 | test_remove_id_field |
|
6 | 0 | 1 | 3 | modules/image/Image_editing/super_resolution/swinir_m_real_sr_x2/test.py | 52,018 | Add swinir_m_real_sr_x2 Module (#2074)
* add swinir_m_real_sr_x2
* update README
* fix typo
* fix typo | PaddleHub | 8 | Python | 6 | test.py | def tearDownClass(cls) -> None:
shutil.rmtree('tests')
shutil.rmtree('swinir_m_real_sr_x2_output')
| 57d977303b4f6002eb8cc40ccb774146921c984a | 19 | https://github.com/PaddlePaddle/PaddleHub.git | 19 | def tearDownClass(cls) -> None:
shutil.r | 4 | 36 | tearDownClass |
|
44 | 0 | 1 | 8 | pandas/tests/reshape/concat/test_index.py | 167,627 | BUG: concat losing columns dtypes for join=outer (#47586) | pandas | 12 | Python | 32 | test_index.py | def test_concat_index_keep_dtype(self, dtype):
# GH#47329
df1 = DataFrame([[0, 1, 1]], columns=Index([1, 2, 3], dtype=dtype))
df2 = DataFrame([[0, 1]], columns=Index([1, 2], dtype=dtype))
result = concat([df1, df2], ignore_index=True, join="outer", sort=True)
expected = DataFrame(
[[0, 1, 1.0], [0, 1, np.nan]], columns=Index([1, 2, 3], dtype=dtype)
)
tm.assert_frame_equal(result, expected)
| 1ac13910aabaabeec0f00319d14d31a08e294475 | 138 | https://github.com/pandas-dev/pandas.git | 103 | def test_concat_index_keep_dtype(self, dtype):
# GH#47329
df1 = DataFrame([[0, 1, 1]], columns=Index([1, 2, 3], dtype=dtype))
df2 = DataFrame([[0, 1]], columns=Index([1, 2], dtype=dtype))
result = concat([df1, df2] | 18 | 190 | test_concat_index_keep_dtype |
|
145 | 0 | 9 | 30 | jina/serve/networking.py | 12,750 | feat: do not await gather endpoints, simply schedule task (#5015) | jina | 18 | Python | 94 | networking.py | async def _get_next_connection(self, num_retries=3):
try:
connection = None
for i in range(len(self._connections)):
internal_rr_counter = (self._rr_counter + i) % len(self._connections)
connection = self._connections[internal_rr_counter]
# connection is None if it is currently being reset. In that case, try different connection
if connection is not None:
break
all_connections_unavailable = connection is None and num_retries <= 0
if all_connections_unavailable:
if num_retries <= 0:
raise EstablishGrpcConnectionError(
f'Error while resetting connections {self._connections}. Connections cannot be used.'
)
elif connection is None:
# give control back to async event loop so connection resetting can be completed; then retry
self._logger.debug(
f' No valid connection found, give chance for potential resetting of connection'
)
try:
await asyncio.wait_for(
self._destroyed_event.wait(),
timeout=GRACE_PERIOD_DESTROY_CONNECTION,
)
finally:
return await self._get_next_connection(num_retries=num_retries - 1)
except IndexError:
# This can happen as a race condition while _removing_ connections
self._rr_counter = 0
connection = self._connections[self._rr_counter]
self._rr_counter = (self._rr_counter + 1) % len(self._connections)
return connection
| 6ba1d165a2aad8e863006be69c813b5cac3d8a21 | 168 | https://github.com/jina-ai/jina.git | 620 | async def _get_next_connection(self, num_retries=3):
try:
connection = None
for i in range(len(self._connections)):
internal_rr_counter = (self._rr_counter + i) % len(self._connections)
connection = self._connections[internal_rr_counter]
# connection is None if it is currently being reset. In that case, try different connec | 21 | 280 | _get_next_connection |
|
43 | 1 | 1 | 8 | tests/gamestonk_terminal/stocks/research/test_res_controller.py | 280,997 | Tests : Stocks > Research + Screener (#1131)
* Updating tests : stocks/research
* Updating tests : stocks/screener
* Updating tests : stocks/screener | OpenBBTerminal | 12 | Python | 37 | test_res_controller.py | def test_print_help():
controller = res_controller.ResearchController(
ticker="MOCK_TICKER",
start=datetime.strptime("2021-12-01", "%Y-%m-%d"),
interval="MOCK_INTERVAL",
queue=None,
)
controller.print_help()
@pytest.mark.vcr(record_mode="none")
@pytest.mark.parametrize(
"an_input, expected_queue",
[
("", []),
("/help", ["quit", "quit", "help"]),
("help/help", ["help"]),
("q", ["quit"]),
("h", []),
(
"r",
[
"quit",
"quit",
"reset",
"stocks",
"load MOCK_TICKER",
"res",
],
),
],
) | 8f8147c3af76f03223943fe630a94dfb326b13c7 | @pytest.mark.vcr(record_mode="none")
@pytest.mark.parametrize(
"an_input, expected_queue",
[
("", []),
("/help", ["quit", "quit", "help"]),
("help/help", ["help"]),
("q", ["quit"]),
("h", []),
(
"r",
[
"quit",
"quit",
"reset",
"stocks",
"load MOCK_TICKER",
"res",
],
),
],
) | 39 | https://github.com/OpenBB-finance/OpenBBTerminal.git | 257 | def test_print_help():
controller = res_controller.ResearchController(
ticker="MOCK_TICKER",
start=datetim | 16 | 216 | test_print_help |
30 | 0 | 3 | 6 | docs/img/plugin-events.py | 224,966 | Add plugin events that persist across builds in `mkdocs serve`
"One-time events" `on_startup(command)`, `on_shutdown`.
Their presence also shows that a plugin *wants* to persist across builds. Otherwise they will be re-created, to not change any existing behavior. | mkdocs | 13 | Python | 29 | plugin-events.py | def event(g, name, parameters):
with cluster(
g, f"cluster_{name}", href=f"#{name}", bgcolor="#ffff3388", pencolor="#00000088"
) as c:
label = "|".join(f"<{p}>{p}" for p in parameters.split())
node(c, name, shape="record" if parameters else "point", label=label, fillcolor="#ffffff55")
| a56ac6e0513bdea6860ed1fdc3debc10410638cd | 72 | https://github.com/mkdocs/mkdocs.git | 56 | def event(g, name, parameters):
with cluster(
g, f"cluster_{name}", href=f"#{ | 16 | 134 | event |
|
126 | 0 | 8 | 39 | tools/infer_vqa_token_ser_re.py | 24,211 | add dygraph2static support of layoutlm series SER model | PaddleOCR | 14 | Python | 81 | infer_vqa_token_ser_re.py | def make_input(ser_inputs, ser_results):
entities_labels = {'HEADER': 0, 'QUESTION': 1, 'ANSWER': 2}
entities = ser_inputs[8][0]
ser_results = ser_results[0]
assert len(entities) == len(ser_results)
# entities
start = []
end = []
label = []
entity_idx_dict = {}
for i, (res, entity) in enumerate(zip(ser_results, entities)):
if res['pred'] == 'O':
continue
entity_idx_dict[len(start)] = i
start.append(entity['start'])
end.append(entity['end'])
label.append(entities_labels[res['pred']])
entities = dict(start=start, end=end, label=label)
# relations
head = []
tail = []
for i in range(len(entities["label"])):
for j in range(len(entities["label"])):
if entities["label"][i] == 1 and entities["label"][j] == 2:
head.append(i)
tail.append(j)
relations = dict(head=head, tail=tail)
batch_size = ser_inputs[0].shape[0]
entities_batch = []
relations_batch = []
entity_idx_dict_batch = []
for b in range(batch_size):
entities_batch.append(entities)
relations_batch.append(relations)
entity_idx_dict_batch.append(entity_idx_dict)
ser_inputs[8] = entities_batch
ser_inputs.append(relations_batch)
# remove ocr_info segment_offset_id and label in ser input
ser_inputs.pop(7)
ser_inputs.pop(6)
ser_inputs.pop(5)
return ser_inputs, entity_idx_dict_batch
| 8d46a1fbbe33d37fc858c53afd0e9fcd9cc185fa | 310 | https://github.com/PaddlePaddle/PaddleOCR.git | 324 | def make_input(ser_inputs, ser_results):
entities_labels = {'HEADER': 0, 'QUESTION': 1, 'ANSWER': 2}
entities = ser_inputs[8][0]
ser_results = ser_results[0]
assert len(entities) == len(ser_results)
# entities
start = []
end = []
label = []
entity_idx_dict = {}
for i, (res, entity) in enumerate(zip(ser_results, entities)):
if res['pred'] == 'O':
continue
entity_idx_dict[len(start)] = i
| 29 | 507 | make_input |
|
7 | 0 | 1 | 3 | homeassistant/components/hunterdouglas_powerview/cover.py | 301,684 | Add support for topdown shades to hunterdouglas_powerview (#62788)
Co-authored-by: J. Nick Koston <[email protected]> | core | 9 | Python | 7 | cover.py | def open_position(self) -> PowerviewShadeMove:
return PowerviewShadeMove(self._shade.open_position, {})
| 45e4dd379b54847174b1f69ca138ba5fe73d24f9 | 20 | https://github.com/home-assistant/core.git | 21 | def open_position(self) -> PowerviewShadeMove:
return PowerviewShadeMove(self._shade.open_position, {})
| 4 | 34 | open_position |
|
178 | 1 | 5 | 48 | keras/utils/composite_tensor_support_test.py | 278,417 | resolve line-too-long in utils | keras | 14 | Python | 112 | composite_tensor_support_test.py | def test_sparse_tensors(self, use_dict, use_dataset, action):
data = [
(
tf.SparseTensor(
[[0, 0, 0], [1, 0, 0], [1, 0, 1]], [1, 2, 3], [2, 1, 3]
),
np.array([[[1, -1, -1]], [[2, 3, -1]]]),
),
(
tf.SparseTensor(
[[0, 0, 0], [1, 0, 0], [1, 0, 1], [2, 0, 1]],
[5, 6, 7, 8],
[3, 1, 4],
),
np.array(
[[[5, -1, -1, -1]], [[6, 7, -1, -1]], [[-1, 8, -1, -1]]]
),
),
]
# Prepare the model to test.
input_name = get_input_name(use_dict)
model_input = input_layer.Input(
shape=(1, None), sparse=True, name=input_name, dtype=tf.int32
)
layers = [ToDense(default_value=-1)]
model = get_model_from_layers_with_input(
layers, model_input=model_input
)
model.compile(
optimizer="sgd",
loss="mse",
metrics=["accuracy"],
**get_test_mode_kwargs()
)
kwargs = get_kwargs(use_dataset, action)
# Prepare the input data
for data_element in data:
input_data, expected_output = prepare_inputs(
data_element, use_dict, use_dataset, action, input_name
)
# Perform the action.
if action == "predict":
result = model.predict(input_data, **kwargs)
self.assertAllEqual(expected_output, result)
if action == "evaluate":
result = model.evaluate(input_data, expected_output, **kwargs)
self.assertAllEqual(1.0, result[-1])
if action == "fit":
# TODO(momernick): What's the best way of validating that fit
# happened?
_ = model.fit(
input_data, expected_output, shuffle=False, **kwargs
)
@test_combinations.run_with_all_model_types
@test_combinations.run_all_keras_modes | 80ee2fa4e1db2dda14370110830db82be3eb97b7 | @test_combinations.run_with_all_model_types
@test_combinations.run_all_keras_modes | 392 | https://github.com/keras-team/keras.git | 803 | def test_sparse_tensors(self, use_dict, use_dataset, action):
data = [
(
tf.SparseTensor(
[[0, 0, 0], [1, 0, 0], [1, 0, 1]], [1, 2, 3], [2, 1, 3]
),
np.array([[[1, -1, -1]], [[2, 3, -1]]]),
),
(
tf.SparseTensor(
[[0, 0, 0], [1, 0, 0], [1, 0, 1], [2, 0, 1]],
[5, 6, 7, 8],
[3, 1, 4],
),
np.array(
[[[5, -1, -1, -1]], [[6, 7, -1, -1]], [[-1, 8, -1, - | 46 | 564 | test_sparse_tensors |
13 | 0 | 2 | 5 | test/prototype_transforms_kernel_infos.py | 194,313 | rename features._Feature to datapoints._Datapoint (#7002)
* rename features._Feature to datapoints.Datapoint
* _Datapoint to Datapoint
* move is_simple_tensor to transforms.utils
* fix CI
* move Datapoint out of public namespace | vision | 12 | Python | 13 | prototype_transforms_kernel_infos.py | def sample_inputs_adjust_hue_image_tensor():
for image_loader in make_image_loaders(
sizes=["random"], color_spaces=(datapoints.ColorSpace.GRAY, datapoints.ColorSpace.RGB)
):
yield ArgsKwargs(image_loader, hue_factor=_ADJUST_HUE_FACTORS[0])
| a8007dcdfb5159a711fa343d2ac4bb7df826975f | 44 | https://github.com/pytorch/vision.git | 32 | def sample_inputs_adjust_hue_image_tensor():
for image_loader in make_image_loaders(
sizes=["random"], color_spaces=(datapoints.ColorSpace.GRAY, datapoints.ColorSp | 12 | 68 | sample_inputs_adjust_hue_image_tensor |
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